Dynamic sound emission for vehicles

ABSTRACT

A vehicle computing system may implement techniques to dynamically adjust a volume and/or frequency of a sound emitted from a vehicle to warn an object (e.g., dynamic object) of a potential conflict with the vehicle. The techniques may include determining a baseline noise level and/or frequencies proximate to the object. The baseline noise level and/or frequencies may be determined based on an identification of one or more noise generating objects in the environment. The vehicle computing system may determine the volume and/or a frequency of the sound based in part on the baseline noise level and/or frequencies, an urgency of the warning, a probability of conflict between the vehicle and the object, a speed of the object, etc.

BACKGROUND

A majority of vehicles in operation today are equipped with horns thatenable an operator of a vehicle to call attention to the vehicle, suchas to warn others of a potential hazard in an environment. Conventionalvehicle horns are configured to emit a sound at a particular frequencyand volume. However, the particular frequency and/or volume of thevehicle horn may often be drowned out by other noises in theenvironment. As such, the vehicle horn may be ineffective in warningothers of the potential hazard. Additionally, the vast majority ofvehicle horns are manually operated, and thus are not configured foreffective use in autonomous vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 is an illustration of an autonomous vehicle in an environment inwhich a dynamic sound emission system of the autonomous vehicle maydetermine a potential conflict between an object in the environment andthe vehicle and emit a sound to alert the object of the potentialconflict, in accordance with embodiments of the disclosure.

FIG. 2 is an illustration of an autonomous vehicle in an environment inwhich a dynamic sound emission system of the autonomous vehicle mayidentify a hazard within a safety zone and activate a warning soundand/or control the autonomous vehicle to avoid the hazard.

FIGS. 3A and 3B are illustrations of example environments and in which avehicle configured with a dynamic sound emission system may operate.FIG. 3A is an illustration of an environment in which the dynamic soundemission system may determine to emit a warning signal based at least inpart on one or more objects being detected. FIG. 3B is an illustrationof an environment in which the dynamic sound emission system maydetermine to not emit a warning signal based at least in part on a lackof objects detected in the environment.

FIG. 4 is a block diagram of an example system for implementing thetechniques described herein.

FIG. 5 depicts an example process for determining at least one of avolume or a frequency of a sound to emit toward an object, in accordancewith embodiments of the disclosure.

FIG. 6 depicts an example process for determining whether an emission ofsound was effective and, based on the effectiveness of the sound,ceasing emission of the sound or causing the vehicle to take an actionto avoid a collision with the object, in accordance with embodiments ofthe disclosure.

FIG. 7 depicts an example process for determining a volume of a sound toemit toward an object, in accordance with embodiments of the disclosure.

FIG. 8 depicts an example process for avoiding a collision between avehicle and an object in an environment by emitting a sound and/orcausing the vehicle to take an action to avoid the collision, inaccordance with embodiments of the disclosure.

FIG. 9 depicts another example process for determining at least one of avolume or a frequency of a sound to emit toward an object, in accordancewith embodiments of the disclosure

DETAILED DESCRIPTION

This disclosure is directed to techniques for improving vehicle warningsystems. The vehicle warning systems may be configured to emit a soundto warn dynamic objects (e.g., agents) in an environment proximate thevehicle of a potential conflict with the vehicle. The vehicle mayinclude an autonomous or semi-autonomous vehicle. The objects mayinclude pedestrians, bicyclists, other vehicles (e.g., cars, trucks,motorcycles, mopeds, etc.), or the like. A vehicle computing system maybe configured to identify an object in the environment and determinethat a potential conflict between the vehicle and the object may occur.The vehicle computing system may determine a baseline amount of noise(e.g., noise floor) around the object and one or more volumes and/or oneor more frequencies of sound to emit toward the object to alert theobject of the potential conflict.

The vehicle computing system may be configured to identify objects inthe environment. In some examples, the objects may be identified basedon sensor data from sensors (e.g., cameras, motion detectors, lightdetection and ranging (lidar), radio detection and ranging (radar),etc.) of the vehicle. In some examples, the objects may be identifiedbased on sensor data received from remote sensors, such as, for example,sensors associated with another vehicle or sensors mounted in anenvironment that are configured to share data with a plurality ofvehicles.

The vehicle computing system may be configured to emit a warning signaltoward one or more objects in the environment. In some examples, thevehicle computing system may emit the warning signal based on adetermination to alert the object(s) of the presence of the vehicle. Forexample, the vehicle computing system may detect a bicyclist on the roadand may determine that the bicyclist may not hear the vehicleapproaching from behind. The vehicle computing system may emit a warningsignal toward the bicyclist, such as to warn the bicyclist of thevehicle's approach so that the bicyclist does not swerve or otherwisemaneuver into the road. The warning signal may include one or moreparticular frequencies and/or volume(s), such as to alert the bicyclistof the vehicle presence, but not cause the bicyclist to becomedisoriented or startled.

In various examples, the vehicle computing system may emit the warningsignal based on a determination of a potential conflict between anobject and the vehicle. In some examples, the vehicle computing systemmay determine a trajectory of the object (e.g., position, velocity,acceleration, etc. of the object as it moves through an environment)based on the sensor data. The potential conflict may be based on acomparison of the trajectory of the object and a speed of the vehiclealong a path through the environment (e.g., trajectory of the vehicle).For example, the vehicle computing system may identify a pedestrian on asidewalk that is approaching a curb with a trajectory indicative of anintent to jaywalk across the road. The vehicle computing system maydetermine that the trajectory of the pedestrian may conflict with thevehicle traveling along a path in the road. Based on the potentialconflict, the vehicle computing system may emit a warning signal towardthe pedestrian to alert the pedestrian of the potential conflict. Invarious examples, the trajectory and/or intent of an object may bedetermined utilizing techniques described in U.S. patent applicationSer. No. 15/947,486 filed Apr. 6, 2018 and entitled “Feature-BasedPrediction,” the entire contents of which are incorporated herein byreference.

In various examples, the vehicle computing system may determine a volumeof the warning signal to emit based on one or more noises in theenvironment. In some examples, the vehicle computing system may beconfigured to determine a noise floor proximate to (e.g., within athreshold distance of) the potentially conflicting object. The noisefloor may be determined based on detecting one or more noise emittingobjects (e.g., music playing on sidewalk, construction equipment, etc.)in the environment and determining a noise floor proximate the object tobe alerted, given the one or more noise emitting objects and relativedistances and speeds to the object to be alerted. In various examples,the vehicle computing system may combine the one or more noises, such asin a logarithmic calculation, to determine the noise floor. The noiseemitting objects may include stationary and/or dynamic objects. Invarious examples, the vehicle computing system may store noise valuesassociated with particular objects. In such examples, the vehiclecomputing system may access the stored noise values to determine a noisefloor proximate to the potentially conflicting object. For example, thevehicle computing system may identify a semi-truck passing 5 meters infront of a pedestrian in the road. The vehicle computing system mayaccess a datastore of noises associated with semi-trucks and determinethat a noise floor 5 meters away from a semi-truck is approximately 75decibels. The vehicle computing system may adjust a volume of thewarning signal to be perceived by the pedestrian at 82 decibels, to belouder than the noise floor to get the attention of the pedestrian.

In various examples, the vehicle computing system may be configured toextrapolate a volume of a noise emitted from a noise emitting objectproximate to the potentially conflicting object based on a perceivednoise of the noise emitting object at the vehicle. In such examples, thevehicle computing system may determine a first distance between thenoise emitting object and the vehicle and a second distance between thenoise emitting object and the potentially conflicting object. Thevehicle computing system may then determine a noise of the noiseemitting object perceived by the vehicle at the first distance andextrapolate a noise value perceived by the potentially conflictingobject at the second distance.

In various examples, the vehicle computing system may adjust a volume ofthe warning signal, as perceived by the potentially conflicting object,based on the noise floor. In such examples, the vehicle computing systemmay emit the warning signal at a volume to be perceived by thepotentially conflicting object at a noise level above the noise floor.In various examples, a volume increase of the warning signal above thenoise floor may be based on a probability of conflict with thepotentially conflicting object (e.g., low, medium, high, etc.). In someexamples, the number of decibel increase in the volume may be based onan urgency of the warning (e.g., low urgency, high urgency, etc.). Forexample, a first warning signal alerting the object, such as thebicyclist described above, of the presence of the vehicle may be abouttwo to about six decibels above the noise floor, whereas a secondwarning signal alerting a jaywalker who has entered a roadway of thepotential conflict with the vehicle may be about fifteen to about twentydecibels above the noise floor.

In some examples, the vehicle computing system may be configured toadjust a volume of the warning signal based on one or more other objectslocated in the environment between the potentially conflicting objectand the vehicle. In such an example, the vehicle computing system mayidentify the other object(s) and determine that the other object(s) arein an audible path (e.g., path of the directed audio beam, beam formedaudio signal) between a speaker of the vehicle emitting the sound andthe potentially conflicting object. The vehicle computing system maydecrease the volume of the warning signal directed at the potentiallyconflicting object to ensure the other object(s) do not sustain hearingdamage and/or other ill effects from the warning signal.

In some examples, the vehicle computing system may be configured toadjust a volume of the warning signal based on a speed associated withthe vehicle. In such examples, the vehicle computing system may increasethe volume associated with the warning signal as the speed of thevehicle increases, and vice versa. In some examples, the volume increasemay be based on ranges of speeds. For example, the warning signal may beemitted at a first volume between 25 and 35 miles per hour, a secondvolume between 36 and 45 miles per hour, and a third volume between 46and 55 miles per hour.

In various examples, the vehicle computing system may be configured todetermine one or more frequencies of the warning signal to emit. Thefrequencies may be based on an urgency associated with the warningsignal, a speed associated with the vehicle and/or the potentiallyconflicting object, a probability of conflict between the vehicle andthe potentially conflicting object, or the like. For example, thevehicle computing system may determine that particular objects, such aspedestrians, are present in the environment and may emit a frequency orrange (e.g., set) of frequencies based on the presence of the particularobjects, to alert the objects of the vehicle operation in theenvironment. As a non-limiting example, the vehicle may detect apedestrian near a large truck and, based on low frequencies emitted fromthe truck's engine, determine to emit frequencies toward the pedestrianat relatively higher frequencies in order to notify the pedestrian. Foranother example, the vehicle computing system may determine that aprobability of conflict (e.g., collision) with a jaywalker that hasentered a road on which the vehicle is traveling is high (e.g., above athreshold probability of conflict) and may emit a frequency or range offrequencies corresponding to an emergency alert.

Additionally or in the alternative, the vehicle computing system may beconfigured to determine an action for the vehicle to perform to avoid aconflict with a potentially conflicting object in the environment. Theaction may include yielding to the potentially conflicting object (e.g.,slowing down or stopping, using emergency braking, etc.), and/orchanging a planned path associated with the vehicle (e.g., lane changeright, lane change left, change planned path of vehicle within lane,drive on shoulder, etc.). In various examples, the vehicle computingsystem may determine the action based on a distance to the potentiallyconflicting object, a speed of the vehicle, a trajectory of thepotentially conflicting object, or the like. For example, the vehiclecomputing system may identify a person on a scooter that is entering aroadway on which the vehicle is traveling. The vehicle computing systemmay determine a trajectory of the scooter, a distance between thevehicle and the scooter, a speed of the vehicle, other traffic on theroadway, and/or options for maneuvering the vehicle. The vehiclecomputing system may determine that, to avoid a collision with thescooter, the vehicle should apply emergency braking. The vehiclecomputing system may cause the vehicle to apply emergency braking, orany other maneuver. For another example, the vehicle computing systemmay determine that a lane change to the right would be a more efficientaction to avoid a collision with the scooter. The vehicle computingsystem may determine that the right lane is clear of other objects andmay cause the vehicle to change lanes. In some examples, the vehiclecomputing system may identify an object that may be affected by thesudden lane change of the vehicle and may emit a warning signal directedto the object. In such examples, the vehicle computing system mayfurther prevent damage to other objects in the environment.

The techniques described herein may be implemented in a number of ways.Example implementations are provided below with reference to thefollowing figures. Although discussed in the context of an autonomousvehicle, the methods, apparatuses, and systems described herein may beapplied to a variety of systems (e.g., a sensor system or a roboticplatform), and are not limited to autonomous vehicles. In anotherexample, the techniques may be utilized in an aviation or nauticalcontext, or in any system using machine vision (e.g., in a system usingimage data). Additionally, the techniques described herein may be usedwith real data (e.g., captured using sensor(s)), simulated data (e.g.,generated by a simulator), or any combination of the two.

FIG. 1 is an illustration of an autonomous (or semi-autonomous) vehicle102 in an environment 100, in which a dynamic sound emission system ofthe autonomous vehicle (vehicle 102) may determine a potential conflictbetween an object 104, such as object 104(1), in the environment 100 andthe vehicle 102, such as vehicle 102(1) and emit a sound to alert theobject 104(1) of the potential conflict. A vehicle computing system mayperform the dynamic sound emission system of the vehicle 102(1). Whiledescribed as a separate system, in some examples, the sound emissionand/or conflict avoidance techniques described herein may be implementedby other vehicle systems, components, and/or computing devices. Forexample, and as will be described in further detail with regard to FIG.4, the sound emission and/or conflict avoidance techniques describedherein may be implemented at least partially by or in associated with aplanning component.

In various examples, the vehicle computing system may be configured todetect one or more objects 104 in the environment 100. The vehiclecomputing system may detect the object(s) 104 based on sensor datareceived from one or more sensors 106. In some examples, the sensor(s)106 may include sensors mounted on the vehicle 102(1), such as sensors106(1), 106(2), 106(3) and 106(4). The sensor(s) 106 may include lidarsensors, radar sensors, ultrasonic transducers, sonar sensors, locationsensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertialmeasurement units (IMUs), accelerometers, magnetometers, gyroscopes,etc.), cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.),microphones, wheel encoders, environment sensors (e.g., temperaturesensors, humidity sensors, light sensors, pressure sensors, etc.), etc.In various examples, each illustrated sensor 106, such as sensors 106(1)and 106(2), may include multiple instances of each of these or othertypes of sensors. In the illustrative example, the vehicles 102 includefour (4) sensors 106. However, in other examples, the vehicles 102 mayinclude a greater or lesser number of sensors 106.

The sensor(s) 106, such as sensors 106(1) and 106(2) may be configuredto collect sensor data over an angle θ. Though illustrated as an angleof less than 180 degrees, the angle θ may include angles of greater orlesser degrees (e.g., any angle between 0 and 360 degrees, etc.). In theillustrative example, the sensors 106(1) and 106(2) collect sensor dataover a same angle θ. In other examples, sensors 106(1) and 106(2) maycollect sensor data over different angles.

In various examples, the vehicle computing system may be configured toreceive sensor data from one or more remote sensors 106. In someexamples, the remote sensor(s) 106 may be mounted on another vehicle102, such as vehicle 102(2). In some examples, the remote sensor(s) 106may include sensor(s) 106 mounted in the environment 100, such as sensor106(5). For example, sensor 106(5) may be placed in the environment 100for traffic monitoring, collision avoidance, or the like. In variousexamples, vehicle 102(1) may be configured to transmit and/or receivedata from vehicle 102(2) and/or remote sensor 106(5). The data mayinclude sensor data, such as data regarding object(s) 104 detected inthe environment 100.

In various examples, the vehicle computing system may receive the sensordata and may determine a type of object 104 (e.g., classification of theobject), such as, for example, whether the object 104 is a pedestrian,such as objects 104(1) and 104(3), a semi-trailer truck, such as object104(2), a motorcycle, a moped, a bicyclist, or the like. In variousexamples, the vehicle computing system may determine a trajectory 108associated with an object 104 detected in the environment 100. In someexamples, the trajectory 108 may include a direction and/or speed thatthe object 104 is traveling through the environment 100.

Based on the determined trajectories 108 associated with object(s) 104,the vehicle computing system may determine that an object 104 maypotentially conflict with the vehicle 102(1) (e.g., potentiallyconflicting object 104(1)). The potentially conflicting object 104(1)may include an object 104 that has a trajectory 108(1) (e.g., either ofa predicted or determined trajectory) that conflicts with a vehicletrajectory 112 (e.g., intersects at a time that, if trajectories 108(1)and 112 remain substantially unchanged, could result in a collisionbetween the vehicle 102(1) and the potentially conflicting object104(1)). The vehicle trajectory 112 may be based on a path, speed and/oraccelerations of the vehicle 102(1) through the environment 100.

In various examples, the vehicle computing system may determine that anobject 104 may potentially conflict with the vehicle 102(1) based on aprobability of conflict between the object 104 and the vehicle 102(1).The probability of conflict may be based on a determined likelihood thatthe object 104 will continue on the trajectory 108(1) and/or alter thetrajectory 108(1) to one that conflicts with the vehicle 102(1). In someexamples, the probability of conflict may correspond to a likelihood(e.g., probability) of conflict between the vehicle 102(1) and theobject 104 being above a threshold level (e.g., threshold probability)of conflict. In some examples, the probability of conflict may bedetermined based on a classification associated with the object 104. Insuch examples, the classification associated with the object 104 mayassist in determining the likelihood that the object 104 will maintainor alter a trajectory. For example, a bicyclist riding on the shoulderof a straight road with no intersections nearby is likely to maintain atrajectory 108 that will not conflict with the vehicle 102(1), as thevehicle can assume that bicycles are incapable of sudden changes inorientation. The bicyclist may thus be determined to not be apotentially conflicting object 104. For another example, a deer detectedon a side of a roadway may be unpredictable and thus may have a highlikelihood of altering a trajectory to conflict with the vehicle 102(1).As such, the deer may be determined to be an object 104 that maypotentially conflict with the vehicle 102(1).

In some examples, a detected loss of the sensor(s) 106 may cause thevehicle computing system to tighten parameters with respect totrajectory predictions. For example, a sensor 106(2) on the vehicle maycease working, leaving an angle of the vehicle with degraded perception(e.g., less sensor data available). To compensate for the loss, thevehicle computing system may decrease tolerances with respect to objectsand predictions of object trajectories, resulting in a more conservativeapproach toward the object(s) 104, which may comprise altering (e.g.,raising) a volume and/or frequencies of sounds emitted.

Based at least in part on determining that a potentially conflictingobject 104(1) exists in the environment 100, the vehicle computingsystem can determine one or more volumes and/or one or more frequencies(e.g., a set/range of frequencies) of a warning signal 110 to emit. Thevolume(s) and/or frequencies of the warning signal 110 may be based on abaseline noise level (e.g., noise floor) in the environment 100 and/or anoise floor proximate to the potentially conflicting object 104(1). Thenoise floor may include a common decibel value and/or decibel range thatrepresents ambient noise in the environment 100 and/or proximate to thepotentially conflicting object 104(1). In various examples, the vehiclecomputing system may determine the volume(s) and/or frequencies of thewarning signal 110 to emit in order to give notice to object(s) 104 inthe environment, such as potentially conflicting object 104(1), that thevehicle computing system is aware of the presence of the object(s) 104in the environment (e.g., notice that the object(s) 104 are detected).

In some examples, the vehicle computing system may determine the noisefloor of the environment 100 and/or the noise floor proximate to thepotentially conflicting object 104(1) based on sensor data, such as fromone or more visual and/or auditory perception sensors. In some examples,the vehicle computing system may receive the sensor data correspondingto objects(s) 104 proximate to potentially conflicting object 104(1) anddetermine a level of noise (e.g., noise event) generated by the object104, such as based on a classification associated the object(s) 104. Thelevel of noise may be determined based on data stored on the vehiclecomputing system and/or a remote computing system and corresponding tothe classification associated with the object(s) 104. In variousexamples, the vehicle computing system may utilize machine learningtechniques to determine the level of noise emitted by the object. Insome examples, a data model may be trained to identify (e.g., classify)object(s) 104 and/or determine, based on a classification of an objectand/or a distance to the potentially conflicting object 104(1), adistinct noise event generated by the object(s) 104.

In some examples, the noise floor(s) may be determined based at least inpart on non-distinct noise events that occur (e.g., perceived by thesensor(s)) in the environment. Non-distinct noise events may includenoises generated from weather (e.g., wind, rain, hail, etc.), vehiclesin the environment (e.g., idling, passing, accelerating, braking, etc.),and/or other noises in the environment 100 (e.g., street music, peopletalking, etc.). In some examples, the noise floor may be determinedbased at least in part on distinct noise events, such as horn honks,whistles blowing, brakes hissing, or the like. In various examples, thenoise floor proximate to the potentially conflicting object 104(1) maybe determined based on detected non-distinct and/or distinct noiseevents emitted from objects within a threshold distance (e.g., 30meters, 50 feet, one block, etc.) of the potentially conflicting object104(1). In some examples, the noise floor may include a logarithmicsummation of the non-distinct and/or distinct noise events that occur inthe environment.

In various examples, the vehicle computing system may receive the sensordata associated with non-distinct and/or distinct noise events and mayapply a smoothing algorithm to the noise events. In some examples, thesmoothing algorithm may smooth the overall impact of short-term (e.g.,distinct) noise events on the noise floor. For example, the vehiclecomputing system may receive sensor data corresponding to a car honkthat causes a 5-decibel spike in the ambient noise. The smoothingalgorithm may be applied to smooth the car honk to 2 decibels, to haveless of an impact on a determined noise floor.

In various examples, the vehicle computing system may determine thenoise floor(s) based on a cumulative distribution of noise events over aperiod of time (e.g., 2 minutes, 5 minutes, 15 minutes, 60 minutes,etc.). For example, the average noise value in an environment at a timemay be 70.5 decibels, the median value may be 69.8 decibels, and themost common value may be 68.3 decibels. Additionally, in the environmentat the time, a large majority (80%) of the noise events may be less than72 decibels. Based on the data, the vehicle computing system maydetermine that, for at least the example environment at the time, thenoise floor in the environment may be between 68-72 decibels.

In various examples, the vehicle computing system may determine thenoise floor in the environment 100 based on a location associated withthe environment 100, a time of day, a day of the week, a time of year(e.g., season, school in or out of session, etc.), in which the vehicle102(1) is traveling in the environment 100, or the like. In someexamples, the vehicle computing system may access a database of noisefloors corresponding to the environment 100 in which the vehicle 102(1)is operating. The database of noise floors may be stored locally and/orremotely, such as on a server computing system or other remote computingsystem. In some examples, the vehicle computing system may receive noisefloor data from one or more remote computing systems, such as, forexample, from a remote computing system associated with remote sensor106(5). In such examples, the remote computing system may be configuredto determine a noise floor in the environment 100 and transmit the noisefloor data to vehicles 102(1) and 102(2) located in proximity to theremote computing device (e.g., within a threshold distance (e.g., 1block, 3 blocks, ¼ mile, etc.) thereof).

In various examples, the vehicle computing system may determine a noisefloor proximate to the potentially conflicting object 104(1). The noisefloor proximate to the potentially conflicting object 104(1) may includea baseline noise level as perceived by the potentially conflictingobject 104(1) (e.g., baseline level of noise the object 104(1) hears).The noise floor proximate to the potentially conflicting object 104(1)may be substantially similar to the noise floor in the environment 100or it may be different.

In various examples, the noise floor proximate to the potentiallyconflicting object 104(1) may be determined and/or augmented based atleast in part on one or more other objects 104 (e.g., dynamic and/ornon-dynamic noise emitting objects) proximate to (e.g., within athreshold distance of) the potentially conflicting object 104(1). Insome examples, the threshold distance may be based on an amount of noiseproduced by the other object(s) 104. For example, music emanating from aspeaker at a storefront may be released at 70 decibels. The speaker mayhave an impact on a noise floor as perceived by the potentiallyconflicting object 104(1) 10 feet away from the speaker, but not 20 feetaway. Thus, the threshold distance from the speaker may be determined tobe at 10 feet.

In various examples, the vehicle computing system may be configured todetermine the amount of noise produced by the other object(s) 104, suchas other object 104(2), proximate to the potentially conflicting object104(1). In some examples, the amount of noise produced by the otherobject(s) 104(2) may be based on a classification associated with theother object(s) 104(2). In various examples, the amount of noiseproduced by a particular class of object 104(2) may be based on valuespreviously perceived by one or more sensors 106 and stored in a databaseof the vehicle computing system and/or one or more remote computingdevices. For example, sensor data from the one or more sensors 106 maybe used to detect an object 104 and may be further used to classify, bythe vehicle computing system, the object 104 as a chopper (e.g., type ofmotorcycle) operating at a constant speed (e.g., minimal acceleration).The vehicle computing system may determine, based on values stored inthe database, that an amount of noise produced by the chopper operatingat a constant speed is 80 decibels. The vehicle computing system mayfurther determine a relative distance between the chopper and thepotentially conflicting object 104(1), as well as the relative distancefrom the vehicle 102 to the object 104(1), so as to determine therelative volume and/or frequencies to emit to the object 104(1).

In various examples, the vehicle computing system may determine theamount of noise based on environmental considerations (e.g., uphill,downhill, acceleration, slowing and/or stopping at an intersection,etc.). For example, a semi-trailer truck approaching an intersection mayutilize engine braking (e.g., jake brake) to slow down. The enginebraking may increase an amount of noise emitted from the semi-trailertruck by 10 decibels or more. The vehicle computing system may factorthe additional noise into the noise floor calculation based on adetermination that the semi-trailer truck is slowing to a stop at theintersection. For another example, traditional motor vehicles travelinguphill have a tendency to downshift and operate an engine at higherrevolutions per minute. The increase in revolutions per minute mayincrease an amount of noise produced by the motor vehicles and/or arelative frequency of noise emitted. As such, the vehicle computingsystem may factor in the additional amount of noise and/or frequencyinto the noise floor calculation of the potentially conflicting object104(1).

In the illustrative example, the noise floor as perceived by thepotentially conflicting object 104(1) is based at least in part on anamount of noise emitted by the other object 104(2), a semi-trailertruck. In various examples, the vehicle computing system may determine adistance D₁ between the potentially conflicting object 104(1) and theother object 104(2). In various examples, the vehicle computing systemmay determine an amount of noise perceived by the potentiallyconflicting object 104(1) based on the distance D₁ (e.g., byextrapolation). For example, the vehicle computing system may determinethat the other object 104(2) emits a 90-decibel noise that decreases 15decibels over the distance D₁, to be perceived by the potentiallyconflicting object 104(1) at 75 decibels. In at least one example, thedecibel decrease over the distance D₁ may be determined using theinverse-square law (1/r²).

In various examples, the vehicle computing system may determine anamount of noise perceived by the potentially conflicting object 104(1)based on a volume of noise perceived by the vehicle 102(1) and adifference between the distance D₁ and a second distance (D₂) betweenthe vehicle 102(1) and the other object 104(2). In various examples,vehicle 102(1) may receive audio signals (e.g., sounds) in theenvironment by one or more directional and/or multi-directionalmicrophones. The vehicle computing system may be configured to correlatethe audio signals to a sensed object 104, such as other object 104(2),in the environment. For example, the vehicle computing system mayperceive a noise emitted by the other object 104(2) at 60 decibels. Thevehicle computing system may correlate the noise with the other object104(2), classified as a semi-truck. The vehicle computing system maydetermine that, based on a distance D₂, the noise volume decreased by 30decibels, and that the actual noise emitted by the other object 104(2)was 90 decibels. The vehicle computing system may determine that thedistance D₂ is greater than the distance D₁. Based on the distance andperceived volume, the vehicle computing system may determine that thepotentially conflicting object 104(1) may perceive the semi-trailernoise at 75 decibels.

In some examples, an impact of a noise produced by the other object(s)104 on the potentially conflicting object 104(1) may be based on thenoise floor in the environment 100. In various examples, the vehiclecomputing system may factor in noises produced by the other object(s)104 to the noise floor proximate to the potentially conflicting object104(1) based on the noises being a threshold volume above the noisefloor in the environment 100. For example, the noise floor in theenvironment 100 may be determined to be 68-72 decibels. The vehiclecomputing system may determine that first object produces a first noisethat is perceived by the potentially conflicting object 104(1) at 60decibels and a second object produces a second noise that is perceivedby the potentially conflicting object 104(1) at 80 decibels. Based on adetermination that the first noise is below the noise floor in theenvironment 100 and the second noise is above the noise floor in theenvironment 100, the vehicle computing system may factor in the secondnoise produced by the second object into the noise floor proximate tothe potentially conflicting object 104(1), but not the first noise.

In various examples, the vehicle computing system may determine afrequency or range (e.g., set) of frequencies of the warning signal 110to emit. In various examples, the frequencies of the warning signal 110may be based on a classification of the potentially conflicting object104(1). For examples, the potentially conflicting object 104(1) may beclassified as a dog. The vehicle computing system may determine a highpitch frequency that is perceptible to dogs but not humans, thusdecreasing an impact on pedestrians, bicyclists, etc. proximate to thepotentially conflicting object 104(1).

In various examples, the vehicle computing system may be configured todetermine a baseline frequency of the noise floor in the environment 100and/or the noise floor proximate to the potentially conflicting object104(1). In some examples, the baseline frequency may include an averagefrequency of the noise events in the environment 100 and/or proximate tothe potentially conflicting object 104(1). In some examples, thebaseline frequency may include a predominant frequency of the noiseevents in the environment 100 and/or proximate to the potentiallyconflicting object 104(1).

In various examples, the vehicle computing system may determine thevolume and/or volume range and/or set of frequencies of the warningsignal 110 based in part on the noise floor and/or baseline frequencyproximate to the potentially conflicting object 104(1). In variousexamples, the vehicle computing system may determine the set offrequencies of the warning signal 110 based on an average and/ordominant frequency in the environment. In such examples, the set offrequencies of the warning signal 110 may substantially differ from theaverage and/or dominant frequency in the environment. In some examples,the vehicle computing system may cause the warning signal 110 to beemitted at a particular volume so that the warning signal 110 isperceived by the potentially conflicting object 104(1) at the volumeand/or volume range. The vehicle computing system may determine theparticular volume to emit the warning signal 110 based on a distance D₃between the vehicle 102(1) and the potentially conflicting object104(1).

In some examples, the volume and/or volume range of the warning signal110 may be higher than the noise floor proximate to the potentiallyconflicting object 104(1). The frequencies of the warning signal 110 maybe higher or lower than the baseline frequency of the noise floorproximate to the potentially conflicting object 104(1). In variousexamples, the frequencies of the warning signal 110 may include afrequency (or set/range of frequencies) that is perceptible to thepotentially conflicting object 104(1), despite ambient noise. The volumeand/or volume range and/or frequencies may be determined based on anurgency of the warning (e.g., low urgency (e.g., alert), medium urgency((e.g., caution), high urgency (e.g., warning)), a likelihood ofconflict between the vehicle 102(1) and the potentially conflictingobject 104(1), a message to be conveyed to the potentially conflictingobject 104(1) (e.g., the vehicle 102(1) is approaching, please stop,trajectories are rapidly converging).

In various examples, the volume and/or volume range and/or frequenciesof the warning signal 110 may be determined based on a detecteddistraction associated with the potentially conflicting object. Thedetected distraction may include the tactile use of a mobile phone, adetermination that the potentially conflicting object is engaged in aconversation (e.g., with another object proximate to the potentiallyconflicting object, on a mobile phone, or the like), a determinationthat the potentially conflicting object is wearing headphones, earmuffs,ear plugs, or any other device configured to fit in or around anauditory canal.

In some examples, the volume and/or volume range and/or frequencies ofthe warning signal 110 may be determined based on weather conditions inthe environment. The weather conditions may include rain, wind, sleet,hail, snow, temperature, humidity, large pressure changes, or any otherweather phenomenon which may affect an auditory perception of an object104 in an environment. In various examples, the volume and/or volumerange and/or frequencies of the warning signal 110 may be determinedbased on road conditions in the environment. The road conditions mayinclude a smoothness of road surface (e.g., concrete, asphalt, gravel,etc.), a number of potholes, uneven terrain (e.g., rumble strips,washboards, corrugation of road, etc.), or the like. For example,objects 104 and/or vehicles 102 operating on a gravel road may generatea larger amount of noise than when operating on a smooth surface. Theincrease in noise generated by the objects 104 and/or vehicles 102(e.g., impact amount of noise from travel) may result in a subsequentincrease in the determined volume and/or volume range of the warningsignal 110.

In various examples, the volume and/or volume range and/or frequenciesof the warning signal 110 may be determined based on a location of thepotentially conflicting agent 104(1) in the environment. For example, ifthe potentially conflicting agent 104(1) is located in a roadway sharedby the vehicle 102(1), the volume and/or volume range may be higher thanif the potentially conflicting agent 104(1) is located on the sidewalk,such as indicating an intent to enter the roadway. For another example,if the potentially conflicting agent 104(1) is a pedestrian standing ona median between opposite direction traffic, the volume and/or volumerange may be higher than if the potentially conflicting agent 104(1) islocated in a bike lane, proximate a curb.

In some examples, the volume and/or volume range and/or frequencies ofthe warning signal 110 may be determined based on a detected loss of oneor more sensors 106 on the vehicle 102(1). For example, the vehiclecomputing system may determine that a speaker on the vehicle is notfunctioning at an optimal capacity. Accordingly, the vehicle computingsystem may increase a volume of the warning signal 110 to compensate forthe decreased capacity of the speaker.

In various examples, the volume and/or volume range and/or frequenciesof the warning signal 110 may be determined based on a detection of apassenger in the vehicle 102(1). In some examples, the detection of thepassenger may be based on sensor data received from one or moresensor(s) 106 of the vehicle. In some examples, the detection of thepassenger may be based on a signal received, such as from a computingdevice associated with the passenger, indicating the passenger presencein the vehicle. In various examples, the vehicle computing system maydecrease the volume and/or volume range and/or frequencies of thewarning signal 110 based on the detection of the passenger, such as, forexample, to not create a negative experience for the passenger due tothe emission of a loud noise.

In various examples, the vehicle computing system may identify anotherobject 104, such as object 104(3), that is located substantially betweenthe vehicle 102(1) and the potentially conflicting object 104(1). Insome examples, the vehicle computing system may determine that atrajectory 108 associated with the other object 104(3), such astrajectory 108(2), does not conflict with the vehicle trajectory 112.However, based on a location of the other object 104(3) beingsubstantially between the vehicle 102(1) and the potentially conflictingagent 104(3), the other object 104(3) may be substantially affected bythe warning signal 110. In various examples, the vehicle computingsystem may adjust the volume and or volume range based on aconsideration associated with the other object 104(3). For example, thevolume and/or volume range of the warning signal 110 perceived by theother object 104(3) may be substantially higher than that perceived bythe potentially conflicting object 104(1). To mitigate a negative effecton the other object 104(3) caused by the warning signal 110, the vehiclecomputing system may decrease a determined volume of the warning signaland/or may use a lower volume in a determined volume range as the volumeto be perceived by the potentially conflicting object 104(1).Additionally or in the alternative, the vehicle computing system mayutilize beam steering in a beam formed array to direct the warningsignal 110 at the potentially conflicting object 104(1). In variousexamples, the vehicle computing system may utilize beam steering and/orbeam formed array techniques discussed in U.S. patent application Ser.No. 14/756,993 entitled “Method for Robotic Vehicle Communication withan External Environment via Acoustic Beam Forming, filed Nov. 4, 2015,and issued as U.S. Pat. No. 9,878,664 on Jan. 30, 2018.

In some examples, the vehicle computing system may cause the warningsignal 110 to be emitted for a pre-determined period of time (e.g., 5seconds, 10 seconds, 20 seconds, etc.). The period of time may be basedon the urgency of the warning, the likelihood of conflict between thevehicle 102(1) and the potentially conflicting object 104(1), themessage to be conveyed to the potentially conflicting object 104(1), aspeed associated with the vehicle 102(1), or the like. For example, analert of the presence of the vehicle (e.g., low urgency) may be emittedfor 10 seconds, and a warning of highly probable conflict (e.g., highurgency) may be emitted for 20 seconds.

In various examples, the vehicle computing system may dynamicallydetermine the period of time associated with warning signal 110emission. In some examples, the period of time may be based on adetermination of a decrease in a likelihood of conflict. In variousexamples, the vehicle computing system may be configured to determine achange to the trajectory 108(1) of the potentially conflicting object inresponse to the warning signal 110. In some examples, the vehiclecomputing system may determine whether the change was sufficient todecrease a likelihood of conflict between the vehicle 102(1) and thepotentially conflicting object 104(1). Based on a determination that thechange in trajectory 108(1) was sufficient to decrease a likelihood ofconflict, such as to a negligible probability of conflict, the vehiclecomputing system may determine that the warning signal 110 is no longernecessary and may cause the warning signal 110 to stop emitting.

In various examples, based on a determination that the change intrajectory 108(1) was not sufficient to decrease the likelihood ofconflict between the vehicle 102(1) and the potentially conflictingobject 104(1), the vehicle computing system may determine to increase avolume and/or a volume range associated with the warning signal 110. Insome examples, the increase in the volume and/or volume range may bebased on a determined escalation of urgency, such as from low urgency tomedium or high urgency, an increase in a likelihood and/or probabilityof conflict, such as from a medium probability to a high probability, orthe like. For example, the vehicle computing system may cause a firstwarning signal 110 to be emitted from the vehicle 102(1) at a firstvolume and/or volume range, to alert the potentially conflicting object104(1) of the vehicle 102(1) operating on the road. The vehiclecomputing system may determine that the trajectory 108(1) associatedwith the potentially conflicting object 104(1) did not substantiallychange as a result of the first warning signal 110. Based on thedetermination of an insufficient change to the trajectory 108(1), thevehicle computing system may cause a second warning signal 110 to beemitted at a second volume and/or second volume range, the second volumeand/or second volume range being greater than the first volume and/orfirst volume range.

In some examples, based on a determination that the change in trajectory108(1) was not sufficient to decrease the likelihood of conflict betweenthe vehicle 102(1) and the potentially conflicting object 104(1), thevehicle computing system may determine to change a frequency and/orrange of frequencies (e.g., higher or lower) of the warning signal 110.In some examples, the frequency adjustment be based on a determinedescalation of urgency, such as from low urgency to medium or highurgency, an increase in a likelihood and/or probability of conflict,such as from a medium probability to a high probability, or the like.For example, the vehicle computing system may cause a first warningsignal 110 to be emitted from the vehicle 102(1) at a first frequency(or set/range of frequencies), to alert the potentially conflictingobject 104(1) of the vehicle 102(1) operating on the road. The vehiclecomputing system may determine that the trajectory 108(1) associatedwith the potentially conflicting object 104(1) did not substantiallychange as a result of the first warning signal 110. Based on thedetermination of an insufficient change to the trajectory 108(1), thevehicle computing system may cause a second warning signal 110 to beemitted at a second frequency (or set/range of frequencies) that ishigher than the first frequency.

Additionally or in the alternative, the vehicle computing system maydetermine to emit a sound (e.g., warning signal 110) based on adetermination that a passenger is entering and/or exiting the vehicle102(1). In some examples, the sound may be emitted when the vehicle isstopped. In such examples, the sound may be used to alert otherobject(s) 104 in the area that the vehicle is altering a passenger load,to inform the other objects(s) 104 that the vehicle 102(1) may beavailable for use, to stimulate a positive response in the otherobject(s) 104, or the like.

Additionally or in the alternative, and as will be discussed in furtherdetail below with regard to FIG. 2, based on a determination that thechange in trajectory 108(1) was not sufficient to decrease thelikelihood of conflict, the vehicle computing system may determine anaction for the vehicle 102(1) to take to avoid the conflict. The actionmay include yielding to the potentially conflicting object 104(1) (e.g.,slowing down or stopping, using emergency braking, etc.), and/orchanging a planned path associated with the vehicle 102(1) (e.g., lanechange right, lane change left, change planned path of vehicle withinlane, drive on shoulder, etc.).

FIG. 2 is an illustration of an environment 200, such as environment100, in which a dynamic sound emission system of the autonomous vehicle202, such as vehicle 102, may identify an object 204(1) that mayconflict with the vehicle 202, such as potentially conflicting object104(1), and activate a warning signal 206, such as warning signal 110and/or control the autonomous vehicle 202 to avoid the object 204(1).

A vehicle computing system of the vehicle 202 may be configured todetect one or more objects 204 and/or one or more stationary objects 208in the environment 200. The vehicle computing system may detect theobject(s) 204 and/or the stationary object(s) 208 based on sensor datareceived from one or more sensors 210. In the illustrative example, thesensor(s) 210 are coupled to the vehicle 202. In some examples, thesensor(s) 210 may additionally or alternatively include sensor(s) 210remotely located in the environment 200. In various examples, thevehicle computing system may detect the object(s) 204 and may determinea classification associated with each object 204. For example, thevehicle computing system may identify object 204(2) as a car operatingin the roadway. For another example, the vehicle computing system maydetect the stationary objects 208(1) and 208(2), and may classify eachas a parked car.

In various examples, the vehicle computing system may determinetrajectories 212 associated with the object(s) 204. In some examples,the trajectory 212 may include a direction and/or speed that the object204 is traveling through the environment 200. In various examples, thevehicle computing system may determine that one of the object(s) 204,such as object 204(1), may potentially conflict with the vehicle 202. Invarious examples, a determination of the potentially conflicting object204(1) may be based on the trajectory 212(1) associated with thepotentially conflicting object 204(1) intersecting with a vehicletrajectory 214. The vehicle trajectory 214 may be determined based on aplanned path 216, speed and/or acceleration of the vehicle 202 operatingin the environment.

In some examples, the determination may be based on the vehicle 202 andthe potentially conflicting object 204(1) maintaining a same orsubstantially similar speed and direction. In some examples, adetermination of the potentially conflicting object 204(1) may be basedon a probability (e.g., likelihood) of conflict between the vehicle 202and the potentially conflicting object 204(1). In some examples, theprobability of conflict may be determined based on the classificationassociated with the object 204(1). In such examples, the classificationassociated with the object 204(1) may assist in determining thelikelihood that the object 204(1) will maintain or alter a trajectory.

Based at least in part on determining that a potentially conflictingobject 204(1) exists in the environment 200, the vehicle computingsystem may determine a location 218 associated with a potential conflict(e.g., a collision). The location 218 may be determined based on thetrajectory 212(1) of the potentially conflicting object 204(1) and/orthe vehicle trajectory 214 remaining the same. In some examples, thelocation 218 may include an area of the vehicle path 216 that thepotentially conflicting object 204(1) travel through on the trajectory212(1).

In various examples, the vehicle computing system may determine that thelocation 218 is outside of a safety zone 220 associated with the vehicle202. The safety zone 220 may include an area in which the vehicle 202,traveling through the environment 200 on the trajectory 214, may applymaximum braking and stop. As illustrated in FIG. 2, the safety zone 220may include a reaction distance (D_(R)) and a braking distance (D_(B)).Both distances (D_(R)) and (D_(B)) may be based on a current speed,and/or acceleration of the vehicle 202, wear on tires and/or brakes ofthe vehicle 202, road materials (e.g., asphalt, concrete, etc.), roadconditions (e.g., potholes, grading, paint strips, etc.), environmentalconsiderations (e.g., rain, snow, hail, etc.), or the like. In variousexamples, the vehicle computing system may receive sensor data from thesensors 210 to determine the safety zone 220. In some examples, thesafety zone 220 associated with a particular area in an environment 200and/or speed may be pre-determined and stored in a database accessibleby the vehicle computing system. In such examples, the vehicle computingsystem may access the stored values periodically (e.g., every minute,every 5 minutes, etc.), at a time of a detected change in conditionand/or speed/acceleration, and/or randomly while operating in anenvironment 200. In at least some examples, such a safety zone 220 maybe determined based on a number of occlusions detected. In someexamples, object 204(1) may be occluded from sensors on vehicle 202 by,for example, object 208(2). In such an example, vehicle 202 maydetermine to emit a sound at a different volume and/or frequency ascompared to other areas due to the risk of objects, such as object204(1) which may be occluded, yet present a high safety risk. In someexamples, the vehicle computing system may implement occlusiondetermination techniques discussed in U.S. patent application Ser. No.16/011,436 entitled “Occlusion Aware Planning” and filed Jun. 18, 2018and U.S. patent application Ser. No. 16/011,468 entitled “OcclusionAware Planning and Control” and filed Jun. 18, 2018, the entire contentsof which are incorporated herein by reference.

In various examples, the vehicle computing system may determine one ormore volumes and/or one or more frequencies of the warning signal 206 toemit. As discussed above, the volume(s) and/or frequencies of thewarning signal 206 may be determined based on a noise floor in theenvironment 200 and/or a noise floor proximate to the potentiallyconflicting object 204(1). In some examples, the volume(s) and/orfrequencies of the warning signal 206 may be based an urgency of thewarning, a likelihood of conflict between the vehicle 202 and thepotentially conflicting object 204(1), and/or a message to be conveyedto the potentially conflicting object 204(1). In various examples, theurgency of the warning, the likelihood of conflict, and/or the messageto be conveyed may be based at least in part on the location 218 withrespect to the safety zone 220. For example, based on a determinationthat the location 218 is located greater than a threshold distance fromthe safety zone 220, the warning signal 206 may include a cautionarysignal (e.g., moderate urgency and/or danger). For another example,based on a determination that the location 218 is located substantiallyproximate to (e.g., within a threshold distance of) the safety zone 220,the warning signal 206 may include an urgent warning (e.g., highlikelihood of conflict, extreme danger, etc.).

Based at least on determining the volume(s) and/or frequencies of thewarning signal 206, the vehicle computing system may cause a firstwarning signal 206(1) to be emitted from the vehicle 202, such as viaone or more speakers 222 coupled to the vehicle 202. In some examples,the first warning signal 206(1) may include an audio beam emitted in adirection relative to the potentially conflicting object 204(1). In someexamples, the first warning signal 206(1) may include a beam formedaudio signal directed at the potentially conflicting object 204(1). Insome examples, the direction relative to the potentially conflictingobject 204(1) may include a position in which the potentiallyconflicting object 204(1) is first detected and/or an anticipatedposition based on the trajectory 212(2). In some examples, the firstwarning signal 206(1) may include an audio beam emitted in a generaldirection associated with the potentially conflicting object 204(1). Thegeneral direction may include an angle (e.g., 45 degrees, 90 degrees,180 degrees, etc.) at which the potentially conflicting object istraveling relative to the vehicle 202. In some examples, the firstwarning signal 206(1) may include an audio signal emitted around thevehicle 202, such as up to and including 360 degrees around the vehicle.

Additionally or in the alternative, the vehicle computing system maydetermine an action for the vehicle 202 to take to avoid the conflictwith the potentially conflicting object 204(1). The action may includeyielding to the potentially conflicting object 204(1) (e.g., slowingdown or stopping, using emergency braking, etc.), and/or changing aplanned path associated with the vehicle 202 (e.g., lane change right,lane change left, change planned path of vehicle within lane, drive onshoulder, etc.). In various examples, the action may be determined,based at least in part on the location 218 and the safety zone 220. Forexample, if the location 218 is located at least a threshold distancefrom the safety zone 220, the vehicle computing system may determinethat the most efficient action to avoid conflict is to stop the vehicle202 to avoid the conflict. For another example, if the location 218 islocated within the safety zone 220, the vehicle 202 may determine thatslowing to a stop will not avoid the conflict. Accordingly, the vehiclecomputing system may cause the vehicle 202 to change lanes to avoid theconflict.

In some examples, the vehicle computing system may determine that theconflict can be avoided by taking two or more actions. In such examples,the action may be determined based on an efficiency associated with eachaction of the two or more actions. In some examples, the efficiency maybe based on a calculated efficiency score corresponding to each action.In such examples, the efficiency score may be based on an amount of timeassociated with each action (e.g., time to stop, avoid the conflict, andaccelerate back up to speed), a probability of conflict with anotherobject 204, such as object 204(2), traffic laws associated with actions,or the like. In various examples, the vehicle computing system maydetermine a particular action based on the particular action beingassociated with a highest efficiency score.

In various example, the vehicle computing system may determine toperform two or more actions, such as to increase a safety marginassociated with the conflict. The safety margin may be based on aprobability (e.g., likelihood) of avoiding the conflict, (e.g., distancebetween the vehicle 202 and the potentially conflicting object 204(1) onthe trajectory 212(1) at a closest point of approach, etc.). Forexample, the vehicle computing system may determine that a lane changeto the right will avoid the conflict and that additionally slowing thevehicle 202 will increase a safety margin associated with the conflict.

Based on the determined action, the vehicle computing system may causethe vehicle 202 to perform the action. In some examples, the action mayinclude slowing or stopping in the vehicle path 216. In some examples,the action may include changing the vehicle path 216, such as a lanechange right or left, altering a position in a current lane, swervingleft or right, such as into a shoulder of the roadway, or the like. Inthe illustrative example, the vehicle computing system causes thevehicle 202 to perform a lane change to the right.

In various examples, based on the determined action, the vehiclecomputing system may identify one or more objects 204 that couldpotentially be affected by the action (e.g., perform subsequent action,as desired). In the illustrative example, the vehicle computing systemmay determine that the object 204(2), located in the right-hand lane,may be affected by the vehicle 202 moving into the right lane. Forexample, the object 204(2) may brake to increase a following distancebehind the vehicle 202.

In various examples, based on an identification of the object 204(2),the vehicle computing system may emit a second warning signal 206(2)directed at the object 204(2) (e.g., in a beam formed audio signal),such as to warn the object 204(2) of the lane change and/or of thepotentially conflicting object 204(1) entering the roadway. A frequency(or set/range of frequencies) and/or volume(s) of the second warningsignal 206(2), similar to the first warning signal 206(1), may be basedon a noise floor (including frequency ranges) in the environment, noisefloor (including frequency ranges) proximate to the object 204(2), anurgency of the warning, a likelihood of conflict, and/or a message to beconveyed. Additionally, the frequency (or set/range of frequencies)and/or volume(s) of the second warning signal 206(2) may be based on aclassification of the object 204(2), an enclosure in which an operatorof the object 204(2) is located, and/or an amount of noise (includingfrequency ranges) experienced by the operator. For example, the vehiclecomputing system may determine that the object 204(2) is classified anon-autonomous hard-top car with the windows rolled up. The vehiclecomputing system may determine that the warning signal 206(2) may beemitted at 80 decibels, for an operator of the car to hear. For anotherexample, the vehicle computing system may determine that the object204(2) is a convertible non-autonomous car operating with the top down.The vehicle computing system may determine that the warning signal206(2) may be emitted at 60 decibels for the operator to hear.

In various examples, the vehicle computing system may determine that anadditional warning signal 206(3) directed toward the potentiallyconflicting object 204(1) may assist in avoiding conflict between thevehicle 202 and the potentially conflicting object 204(1). In suchexamples, the vehicle computing system may determine a volume (orset/range of volumes) and/or a frequency (or set/range of frequencies)for the additional warning signal 206(3). The volume(s) and/orfrequencies may be the same or different from the first warning signal206(1).

FIGS. 3A and 3B are illustrations of example environments 300 and 302 inwhich a vehicle 304, such as vehicle 102, configured with a dynamicsound emission system may operate. FIG. 3A is an illustration of anenvironment 300 in which one or more objects 306, such as objects 104are detected. As discussed above, a vehicle computing system of thevehicle 304 may be configured to detect the object(s) 306 based at leastin part on sensor data received from one or more sensors. In someexamples, the vehicle computing device may be configured to identify atype of object (e.g., classification) associated with each of thedetected object(s).

As illustrated in FIG. 3A, based in part on the detection of theobject(s) 306, the vehicle computing system may determine to emit awarning signal 308, such as warning signal 110, to alert the object(s)306 of the vehicle presence. In some examples, a determination to emitthe warning signal 308 may be based on a detection a particular type ofobject 306 detected. For example, the vehicle computing system mayidentify objects 306 classified as cars and trucks operating on aroadway of an environment. Based on a determination that only cars andtrucks are in the environment, the vehicle computing device maydetermine to not emit the warning signal 308. For another example, thevehicle computing system may identify objects 306 classified aspedestrians and bicyclists in the environment. Based on thedetermination that pedestrians and bicyclists are in the environment,the vehicle computing system may determine to emit the warning signal308.

In various examples, a determination to emit the warning signal 308 maybe based on a proximity of at least one object 306 of the object(s) 306to the vehicle 304(1) and/or a path of the vehicle 304(1). In someexamples, the at least one object 306 may include an object that islocated closest (e.g., a shortest distance) to the vehicle 304(1) and/ora path of the vehicle 304(1). In some examples, the determination toemit the warning signal may be based on the at least one object 306being within a threshold distance (e.g., 1 block, 100 yards, 200 meters,etc.) of the vehicle and/or the path of the vehicle 304(1). In variousexamples, the determination to emit the warning signal may be based onthe object(s) 306 being located on a side of a roadway in which thevehicle 304(1) is operating. For example, as illustrated in FIG. 3A, thevehicle is operating in the right lane on the right side of a roadway.The vehicle computing system may determine to emit the warning signal308 based on the detection of objects 306 on the right side of the roadand/or the classification associated with the objects 306 located on theright side of the road.

In various examples, the warning signal 308 may include one or moredirected audio signals emitted toward the object(s) 306 (e.g., directbeam sent in the direction of the object). In some examples, the warningsignal 308 may be emitted directly at one or more of the object(s) 306,such as by utilizing beam forming techniques described above. In theillustrative example, the warning signal 308 is emitted in directionscorresponding to the four corners of the car. In some examples, thewarning signal may be emitted in specific quadrants (e.g., front right,back left, etc.) corresponding to the location of the object(s) 306. Insome examples, the warning signal 308 may be emitted on a side of thevehicle 304(1) (e.g., right side or left side) corresponding to thelocation of the object(s) 306. In various examples, the warning signal308 may be emitted at any angle up to an including 360 degrees aroundthe vehicle, such as 30, 45, 60, 90, 360 degrees, etc.

In various examples, the warning signal 308 may be emitted to alert theobject(s) 306 of the presence of the vehicle 304(1). For example, anelectrically operated vehicle may produce a negligible amount ofoperating noise and may be substantially aurally imperceptible toobjects 306 in the environment 300. Accordingly, the warning signal 308may provide a means by which the object(s) 306 may determine thepresence of the vehicle 304(1). A frequency or set of frequencies (e.g.,waveform) of the warning signal 308 may be determined according totechniques described above with respect to FIGS. 1 and 2. In variousexamples, the frequencies of the warning signal 308 may be determinedbased on the purpose of alerting the object(s) 306 of the presence ofthe vehicle 304(1). In some examples, the frequencies may be determinedbased on an impression the warning signal 308 may have on the object(s)306. For example, the frequencies may be associated with a soft, melodicsound that is intended to leave the object(s) 306 with a positiveimpression of the vehicle 304(1). For another example, the frequenciesin a set of frequencies may be selected based on a determination toemulate the sound of a gas-powered vehicle engine. In such an example,the frequencies of the warning signal 308 may adjust higher and lower tosimulate an engine adjusting revolutions per minute while operating inthe environment.

A volume (or set/range of volumes) of the warning signal 308 may bedetermined in accordance with volume determinations described above withrespect to FIGS. 1 and 2. In various examples, the volume(s) of thewarning signal 308 may be determined based at least in part on a speedassociated with the vehicle. In some examples, the volume(s) of thewarning signal 308 may increase as the speed of the vehicle 304(1)increases, or vice versa. For example, the vehicle computing system maydetermine to emit a warning signal 308 at 50 decibels based on adetermined vehicle 304(1) speed of 15 miles per hour. After increasingthe speed to 30 miles per hour, the vehicle computing system maydetermine to emit the warning signal 308 at 70 decibels.

In various examples, the frequencies and/or volume(s) of the warningsignal 308 may be determined based on an area in which the vehicle304(1) is operating and/or a time (e.g., time of the day, of the week,of the month, of the year, etc.) associated with the operation. Forexample, the vehicle computing system may determine that the vehicle304(1) is operating in a school zone during a school day (e.g., duringschool hours). Based on an operation in the school zone during schoolhours, the vehicle computing system may select a frequency (or set/rangeof frequencies) of warning signal 308 that may be audibly appealing to(e.g., catch the attention of) school children.

In various examples, based on a determination that objects 306 and/orobjects 306 of a particular type are no longer detected proximate to thevehicle 304(1), the vehicle computing system may determine to not emitthe warning signal 308.

FIG. 3B is an illustration of an environment 302 in which the dynamicsound emission system may determine to not emit a warning signal, suchas warning signal 308, based at least in part on a lack of objects, suchas objects 306, and/or a lack of a particular type of objects detectedin the environment 302.

In the illustrative example of FIG. 3B, the environment 302 includes ahighway-type environment in which the vehicle 304(1) and another vehicle304(2) may operate. In some examples, based on a determination that noobjects exist in the environment 302, the vehicle computing system ofthe vehicle 304(1) may determine to not emit the warning signal. In someexamples, the environment 302 may include objects classified as cars,motorcycles, trucks, and/or other motorized or electric vehiclesconfigured for travel on a roadway. In such examples, based on adetermination that no objects classified as pedestrians, bicyclists,scooters, or other objects that are self-powered are present in theenvironment 302, the vehicle computing device may determine to not emitthe warning signal.

In some examples, a determination to not emit the warning signal may bebased at least in part on a speed associated with the vehicle 304(1). Insuch examples, the vehicle computing device may determine that the speedof the vehicle has increased above an upper threshold value (e.g., 50miles per hour, 60 miles per hour, 65 miles per hour, etc.) or decreasedbelow a lower threshold value (e.g., 15 miles per hour, 10 miles perhour, 7 miles per hour, etc.), the vehicle computing device maydetermine to not emit the warning signal. In some examples, the upperthreshold value may be determined based on a probability (e.g.,likelihood) of conflict with an object and/or presence of a potentiallyconflicting object. In some examples, the lower threshold value may bedetermined based on a distance required to stop the vehicle 304(1) atmaximum (e.g., emergency) braking. For example, the vehicle computingdevice may determine that on a road in which the vehicle 304(1) maytravel at 55 miles per hour, a likelihood of detecting objectsclassified as pedestrians is below a threshold likelihood. Accordingly,the vehicle computing device may determine to not emit the warningsignal. For another example, the vehicle computing device may determinethat the speed of the vehicle 304(1) has slowed below 10 miles per hour(e.g., a lower threshold speed). Based on a determination that thevehicle is below the lower threshold speed, the vehicle computing devicemay determine to not emit the warning signal.

FIG. 4 is a block diagram of an example system 400 for implementing thetechniques described herein. In at least one example, the system 400 mayinclude a vehicle 402, such as vehicle 102.

The vehicle 402 may include one or more vehicle computing devices 404(e.g., vehicle computing system), one or more sensor systems 406, one ormore emitters 408, one or more communication connections 410, at leastone direct connection 412, and one or more drive modules 414.

The vehicle computing device(s) 404 may include one or more processors416 and memory 418 communicatively coupled with the one or moreprocessors 416. In the illustrated example, the vehicle 402 is anautonomous vehicle; however, the vehicle 402 could be any other type ofvehicle, such as a semi-autonomous vehicle, or any other system havingat least an image capture device (e.g., a camera enabled smartphone). Inthe illustrated example, the memory 418 of the vehicle computingdevice(s) 404 stores a localization component 420, a perceptioncomponent 422, a planning component 424, one or more system controllers426, and a warning signal component 428 including an object trajectorycomponent 430, a risk component 432, a frequency component 434, a volumecomponent 436, and an action component 438. Though depicted in FIG. 4 asresiding in the memory 418 for illustrative purposes, it is contemplatedthat the localization component 420, a perception component 422, aplanning component 424, one or more system controllers 426, and awarning signal component 428 including an object trajectory component430, a risk component 432, a frequency component 434, a volume component436, and an action component 438 may additionally, or alternatively, beaccessible to the vehicle 402 (e.g., stored on, or otherwise accessibleby, memory remote from the vehicle 402, such as, for example, on memory440 of a remote computing device 442).

In at least one example, the localization component 420 may includefunctionality to receive data from the sensor system(s) 406 to determinea position and/or orientation of the vehicle 402 (e.g., one or more ofan x-, y-, z-position, roll, pitch, or yaw). For example, thelocalization component 420 may include and/or request/receive one ormore map(s) of an environment and may continuously determine a locationand/or orientation of the autonomous vehicle within the map(s). For thepurpose of this discussion, a map may be any number of data structuresmodeled in two dimensions, three dimensions, or N-dimensions that arecapable of providing information about an environment, such as, but notlimited to, topologies (such as intersections), streets, mountainranges, roads, terrain, and the environment in general. In someinstances, a map may include, but is not limited to: texture information(e.g., color information (e.g., RGB color information, Lab colorinformation, HSV/HSL color information), and the like), intensityinformation (e.g., lidar information, radar information, and the like);spatial information (e.g., image data projected onto a mesh, individual“surfels” (e.g., polygons associated with individual color and/orintensity)), reflectivity information (e.g., specularity information,retroreflectivity information, BRDF information, BSSRDF information, andthe like). In at least one example, a map may include athree-dimensional mesh of the environment. In some examples, the vehicle402 may be controlled based at least in part on the map(s). That is, themap(s) may be additionally used in connection with the perceptioncomponent 422 and/or the planning component 424 to determine a locationof the vehicle 402, detect objects in an environment, and/or generateroutes and/or trajectories to navigate within an environment.

In some examples, the one or more maps may be stored on a remotecomputing device(s) (such as the computing device(s) 442) accessible vianetwork(s) 444. In some examples, multiple maps may be stored based on,for example, a characteristic (e.g., type of entity, time of day, day ofweek, season of the year, etc.). Storing multiple maps may have similarmemory requirements but increase the speed at which data in a map may beaccessed.

In various examples, the localization component 420 may be configured toutilize SLAM (simultaneous localization and mapping), CLAMS(calibration, localization and mapping, simultaneously), relative SLAM,bundle adjustment, non-linear least squares optimization, or the like toreceive image data, lidar data, radar data, IMU data, GPS data, wheelencoder data, and the like to accurately determine a location of thevehicle 402. In some instances, the localization component 420 mayprovide data to various components of the vehicle 402 to determine aninitial position of an autonomous vehicle for determining a likelihood(e.g., probability) of conflict with an object, as discussed herein.

In some examples, the perception component 422 may include functionalityto perform object detection, segmentation, and/or classification. Insome examples, the perception component 422 may provide processed sensordata that indicates a presence of an object (e.g., entity, dynamicobject) that is proximate to the vehicle 402 and/or a classification ofthe object as an object type (e.g., car, pedestrian, cyclist, dog, cat,deer, unknown, etc.). In some examples, the perception component 422 mayprovide processed sensor data that indicates a presence of a stationaryentity that is proximate to the vehicle 402 and/or a classification ofthe stationary entity as a type (e.g., building, tree, road surface,curb, sidewalk, unknown, etc.). In additional or alternative examples,the perception component 422 may provide processed sensor data thatindicates one or more characteristics associated with a detected object(e.g., a tracked object) and/or the environment in which the object ispositioned. In some examples, characteristics associated with an objectmay include, but are not limited to, an x-position (global and/or localposition), a y-position (global and/or local position), a z-position(global and/or local position), an orientation (e.g., a roll, pitch,yaw), an object type (e.g., a classification), a velocity of the object,an acceleration of the object, an extent of the object (size), etc.Characteristics associated with the environment may include, but are notlimited to, a presence of another object in the environment, a state ofanother object in the environment, a time of day, a day of a week, aseason, a weather condition (e.g., rain, sleet, hail, snow, temperature,humidity, etc.), an indication of darkness/light, etc.

In general, the planning component 424 may determine a path for thevehicle 402 to follow to traverse through an environment. For example,the planning component 424 may determine various routes and trajectoriesand various levels of detail. For example, the planning component 424may determine a route to travel from a first location (e.g., a currentlocation) to a second location (e.g., a target location). For thepurpose of this discussion, a route may include a sequence of waypointsfor travelling between two locations. As non-limiting examples,waypoints include streets, intersections, global positioning system(GPS) coordinates, etc. Further, the planning component 424 may generatean instruction for guiding the vehicle 402 along at least a portion ofthe route from the first location to the second location. In at leastone example, the planning component 424 may determine how to guide thevehicle 402 from a first waypoint in the sequence of waypoints to asecond waypoint in the sequence of waypoints. In some examples, theinstruction may be a trajectory, or a portion of a trajectory. In someexamples, multiple trajectories may be substantially simultaneouslygenerated (e.g., within technical tolerances) in accordance with areceding horizon technique, wherein one of the multiple trajectories isselected for the vehicle 402 to navigate.

In some examples, the planning component 424 may include a predictioncomponent to generate predicted trajectories of objects in anenvironment. For example, a prediction component may generate one ormore predicted trajectories for objects within a threshold distance fromthe vehicle 402. In some examples, a prediction component may measure atrace of an object and generate a trajectory for the object based onobserved and predicted behavior.

In at least one example, the vehicle computing device(s) 404 may includeone or more system controllers 426, which may be configured to controlsteering, propulsion, braking, safety, emitters, communication, andother systems of the vehicle 402. The system controller(s) 426 maycommunicate with and/or control corresponding systems of the drivemodule(s) 414 and/or other components of the vehicle 402.

As illustrated in FIG. 4, the vehicle computing device(s) 404 mayinclude a warning signal component 428. The warning signal component 428may include an object trajectory component 430 configured to determinetrajectories of objects in the environment. An object trajectory mayinclude a direction and/or speed the object may travel from a currentposition (e.g., at the time of perception) and/or based on a directionof travel. In some examples, the object trajectory component 430 maydetermine that an object is within a threshold distance (e.g., oneblock, 200 meters, 300 feet, etc.) of the vehicle 402. Based on thedetermination that the object is within the threshold distance to thevehicle, the object trajectory component 430 may determine thetrajectories associated with the object.

In various examples, the object trajectory component 430 may receivetrajectory data from a prediction component of the planning component424. In some examples, the object trajectory component 430 may beconfigured to receive sensor data from the sensor system(s) 406 and/orobject data corresponding to detected objects and/or particular types ofobjects (e.g., pedestrians, bicyclists, etc.) from the perceptioncomponent 422. In such examples, the object trajectory component 430 mayprocess the received data and measure a trace of an object in order togenerate a trajectory for the object based on observed and predictedbehavior.

The warning signal component 428 may include a risk component 432configured to identify one or more objects in an environment that maypotentially conflict with the vehicle 402 (e.g., potentially conflictingobject(s)). As discussed above, a risk (e.g., probability, likelihood,etc.) of conflict may be based on a relationship between a determinedvehicle trajectory and a determined object trajectory. In some examples,the risk may be determined based on a likelihood of the vehicle 402 andthe potentially conflicting object occupying the same or a similar spacein the environment at the same or a substantially similar time. Invarious examples, the risk of conflict may be based on a safety zoneassociated with the vehicle 402 as discussed above with regard to FIG.2.

In various examples, the risk component 432 may receive objecttrajectory data from the object trajectory component 432 and/or theplanning component. In some examples, the risk component 432 may receivelocation data and/or vehicle trajectory data from the localizationcomponent 420, the perception component 422, and/or the planningcomponent 424. The risk component 432 may process the vehicle and objecttrajectory data to determine the risk of conflict between the vehicle402 and an object.

In various examples, the frequency component 434 may be configured todetermine one or more frequencies of a warning signal to emit. Asdiscussed above, the frequencies may be determined based on a detectionof objects in the environment, a detection of a particular type ofobject, a determination of a potentially conflicting object, a speed ofthe vehicle 402, an impression the warning signal may have on objects inthe environment, locations and/or speeds of the detected objects in theenvironment, or the like. In various examples, the frequency component434 may access a database of warning signal frequencies to determine thefrequency and/or set of frequencies associated with the warning signal.In some examples, the database of warning signal frequencies may bestored on the memory 418. In some examples, the database of warningsignal frequencies may be stored on a memory 440 of the remote computingdevice(s) 442 and accessible via the network(s) 444.

In various examples, the volume component 436 of the warning signalcomponent 428 may determine one or more volumes of the warning signal.In some examples, the volume(s) may be based on a noise floor of theenvironment as perceived by the vehicle. In some examples, the volume(s)may be based on a noise floor as perceived by the potentiallyconflicting object to which the warning signal is directed. In someexamples, the volume(s) may be additionally, or alternatively, based ona distance between the vehicle 402 and the potentially conflictingobject. Furthermore, and as discussed above, the volume(s) may be basedon one or more objects being detected in the environment, such as thoselocated between the vehicle 402 and the potentially conflicting object,a speed of the vehicle, a type of object to which the warning signal isdirected (e.g., car, pedestrian, etc.), a speed and/or location of theobject relative to the potentially conflicting object, a conditionassociated with the object (e.g., pedestrian wearing headphones orearmuffs, a pedestrian having a conversation in person and/or on thephone, an operator of a convertible car, a hard-top car, a vehicle thatgenerates a substantial amount of noise (e.g., semi-trailer truck,etc.), etc.).

In various examples, the action component 438 may, based on adetermination of risk (e.g., high, medium, or low risk), determine toemit the warning signal. The warning signal may be emitted at thefrequency (or set/range of frequencies) determined by the frequencycomponent 434 and/or the volume(s) determined by the volume component436. Based on the determination to emit a warning signal, the vehiclecomputing device(s) 404, such as through the sensor systems 406, mayemit the warning signal. The warning signal may be emitted in adirection determined by the action component 438, such as directed inany angle up to and including 360 degrees around the vehicle 402. Invarious examples, the action component 438 may be configured to causemultiple warning signals to be emitted in multiple directions. Forexample, the warning signal component 428 may identify two potentiallyconflicting objects. The warning signal component 428 may determine arisk associated with each of the potentially conflicting objects and maydetermine a frequency (or set/range of frequencies) and/or volume (orset/range of volumes) of each warning signal to emit based at least inpart on the risk associated therewith. The action component 438 maycause the respective warning signals to be emitted in respectivedirections of the potentially conflicting objects.

In various examples, the action component 438 may, based on thedetermination of risk (e.g., high, medium, or low risk), determine anaction for the vehicle to take. The action may include slowing thevehicle to yield to the object, stopping the vehicle to yield to theobject, changing lanes left, or changing lanes right. Based on thedetermined action, the vehicle computing device(s) 404, such as throughthe system controller(s) 426, may cause the vehicle to perform theaction. In at least some examples, such an action may be based on theprobability of collision, as described in detail above.

As can be understood, the components discussed herein (e.g., thelocalization component 420, the perception component 422, the planningcomponent 424, the one or more system controllers 426, the warningsignal component 428 including the object trajectory component 430, therisk component 432, the frequency component 434, the volume component436, and the action component 438 are described as divided forillustrative purposes. However, the operations performed by the variouscomponents may be combined or performed in any other component.

In some instances, aspects of some or all of the components discussedherein may include any models, techniques, and/or machine learningtechniques. For example, in some instances, the components in the memory418 (and the memory 440, discussed below) may be implemented as a neuralnetwork. As described herein, an exemplary neural network is abiologically inspired technique which passes input data through a seriesof connected layers to produce an output. Each layer in a neural networkmay also comprise another neural network, or may comprise any number oflayers (whether convolutional or not). As can be understood in thecontext of this disclosure, a neural network may utilize machinelearning, which may refer to a broad class of such techniques in whichan output is generated based on learned parameters.

In some examples, the vehicle computing device(s) 404 may utilizemachine learning techniques to determine one or more volumes and/or oneor more frequencies of noises emitted by identified (e.g., perceived,classified, etc.) objects. In some examples, one or more data models maybe trained to determine a noise emitted by an identified object based onone or more conditions in the environment. The condition(s) may includeterrain features, a road condition (e.g., gravel, smooth pavement,etc.), speed and/or acceleration of the identified object, weatherconditions, or the like. In various examples, the data model(s) may betrained to output a noise (e.g., noise level, an number of decibels,etc.) emitted by an object based at least in part on the condition(s)present in the environment.

Although discussed in the context of neural networks, any type ofmachine learning may be used consistent with this disclosure. Forexample, machine learning techniques may include, but are not limitedto, regression techniques (e.g., ordinary least squares regression(OLSR), linear regression, logistic regression, stepwise regression,multivariate adaptive regression splines (MARS), locally estimatedscatterplot smoothing (LOESS)), instance-based techniques (e.g., ridgeregression, least absolute shrinkage and selection operator (LASSO),elastic net, least-angle regression (LARS)), decisions tree techniques(e.g., classification and regression tree (CART), iterative dichotomiser3 (ID3), Chi-squared automatic interaction detection (CHAID), decisionstump, conditional decision trees), Bayesian techniques naïve Bayes,Gaussian naïve Bayes, multinomial naïve Bayes, average one-dependenceestimators (AODE), Bayesian belief network (BNN), Bayesian networks),clustering techniques (e.g., k-means, k-medians, expectationmaximization (EM), hierarchical clustering), association rule learningtechniques (e.g., perceptron, back-propagation, hopfield network, RadialBasis Function Network (RBFN)), deep learning techniques (e.g., DeepBoltzmann Machine (DBM), Deep Belief Networks (DBN), ConvolutionalNeural Network (CNN), Stacked Auto-Encoders), Dimensionality ReductionTechniques (e.g., Principal Component Analysis (PCA), PrincipalComponent Regression (PCR), Partial Least Squares Regression (PLSR),Sammon Mapping, Multidimensional Scaling (MDS), Projection Pursuit,Linear Discriminant Analysis (LDA), Mixture Discriminant Analysis (MDA),Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis(FDA)), Ensemble Techniques (e.g., Boosting, Bootstrapped Aggregation(Bagging), AdaBoost, Stacked Generalization (blending), GradientBoosting Machines (GBM), Gradient Boosted Regression Trees (GBRT),Random Forest), SVM (support vector machine), supervised learning,unsupervised learning, semi-supervised learning, etc. Additionalexamples of architectures include neural networks such as ResNet70,ResNet101, VGG, DenseNet, PointNet, and the like.

In at least one example, the sensor system(s) 406 may include lidarsensors, radar sensors, ultrasonic transducers, sonar sensors, locationsensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertialmeasurement units (IMUs), accelerometers, magnetometers, gyroscopes,etc.), cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.),microphones, wheel encoders, environment sensors (e.g., temperaturesensors, humidity sensors, light sensors, pressure sensors, etc.), etc.The sensor system(s) 406 may include multiple instances of each of theseor other types of sensors. For instance, the lidar sensors may includeindividual lidar sensors located at the corners, front, back, sides,and/or top of the vehicle 402. As another example, the camera sensorsmay include multiple cameras disposed at various locations about theexterior and/or interior of the vehicle 402. The sensor system(s) 406may provide input to the vehicle computing device(s) 404. Additionallyor alternatively, the sensor system(s) 406 may send sensor data, via theone or more networks 444, to the one or more computing device(s) 442 ata particular frequency, after a lapse of a predetermined period of time,in near real-time, etc.

The vehicle 402 may also include one or more emitters 408 for emittinglight and/or sound, as described above. The emitters 408 in this exampleinclude interior audio and visual emitters to communicate withpassengers of the vehicle 402. By way of example and not limitation,interior emitters may include speakers, lights, signs, display screens,touch screens, haptic emitters (e.g., vibration and/or force feedback),mechanical actuators (e.g., seatbelt tensioners, seat positioners,headrest positioners, etc.), and the like. The emitters 408 in thisexample also include exterior emitters. By way of example and notlimitation, the exterior emitters in this example include lights tosignal a direction of travel or other indicator of vehicle action (e.g.,indicator lights, signs, light arrays, etc.), and one or more audioemitters (e.g., speakers, speaker arrays, horns, etc.) to audiblycommunicate with pedestrians or other nearby vehicles, one or more ofwhich comprising acoustic beam steering technology.

The vehicle 402 may also include communication connection(s) 410 thatenable communication between the vehicle 402 and one or more other localor remote computing device(s) 442. For instance, the communicationconnection(s) 410 may facilitate communication with other localcomputing device(s) on the vehicle 402 and/or the drive module(s) 414.Also, the communication connection(s) 410 may allow the vehicle tocommunicate with other nearby computing device(s) (e.g., computingdevice(s) 442, other nearby vehicles, etc.) and/or one or more remotesensor system(s) 446 for receiving sensor data.

The communications connection(s) 410 may include physical and/or logicalinterfaces for connecting the vehicle computing device(s) 404 to anothercomputing device or a network, such as network(s) 444. For example, thecommunications connection(s) 410 can enable Wi-Fi-based communicationsuch as via frequencies defined by the IEEE 802.11 standards, shortrange wireless frequencies such as Bluetooth, cellular communication(e.g., 2G, 3G, 4G, 4G LTE, 5G, etc.) or any suitable wired or wirelesscommunications protocol that enables the respective computing device tointerface with the other computing device(s).

In at least one example, the vehicle 402 may include one or more drivemodules 414. In some examples, the vehicle 402 may have a single drivemodule 414. In at least one example, if the vehicle 402 has multipledrive modules 414, individual drive modules 414 may be positioned onopposite ends of the vehicle 402 (e.g., the front and the rear, etc.).In at least one example, the drive module(s) 414 may include one or moresensor systems to detect conditions of the drive module(s) 414 and/orthe surroundings of the vehicle 402. By way of example and notlimitation, the sensor system(s) may include one or more wheel encoders(e.g., rotary encoders) to sense rotation of the wheels of the drivemodules, inertial sensors (e.g., inertial measurement units,accelerometers, gyroscopes, magnetometers, etc.) to measure orientationand acceleration of the drive module, cameras or other image sensors,ultrasonic sensors to acoustically detect objects in the surroundings ofthe drive module, lidar sensors, radar sensors, etc. Some sensors, suchas the wheel encoders may be unique to the drive module(s) 414. In somecases, the sensor system(s) on the drive module(s) 414 may overlap orsupplement corresponding systems of the vehicle 402 (e.g., sensorsystem(s) 406).

The drive module(s) 414 may include many of the vehicle systems,including a high voltage battery, a motor to propel the vehicle, aninverter to convert direct current from the battery into alternatingcurrent for use by other vehicle systems, a steering system including asteering motor and steering rack (which can be electric), a brakingsystem including hydraulic or electric actuators, a suspension systemincluding hydraulic and/or pneumatic components, a stability controlsystem for distributing brake forces to mitigate loss of traction andmaintain control, an HVAC system, lighting (e.g., lighting such ashead/tail lights to illuminate an exterior surrounding of the vehicle),and one or more other systems (e.g., cooling system, safety systems,onboard charging system, other electrical components such as a DC/DCconverter, a high voltage junction, a high voltage cable, chargingsystem, charge port, etc.). Additionally, the drive module(s) 414 mayinclude a drive module controller which may receive and preprocess datafrom the sensor system(s) 406 and to control operation of the variousvehicle systems. In some examples, the drive module controller mayinclude one or more processors and memory communicatively coupled withthe one or more processors. The memory 418 may store one or more modulesto perform various functionalities of the drive module(s) 414.Furthermore, the drive module(s) 414 may also include one or morecommunication connection(s) that enable communication by the respectivedrive module with one or more other local or remote computing device(s)442.

In at least one example, the direct connection 412 may provide aphysical interface to couple the one or more drive module(s) 414 withthe body of the vehicle 402. For example, the direct connection 412 mayallow the transfer of energy, fluids, air, data, etc. between the drivemodule(s) 414 and the vehicle. In some instances, the direct connection412 may further releasably secure the drive module(s) 414 to the body ofthe vehicle 402.

In at least one example, the localization component 420, the perceptioncomponent 422, the planning component 424, the one or more systemcontrollers 426, and the warning signal component 428 and variouscomponents thereof, may process sensor data, as described above, and maysend their respective outputs, over the one or more network(s) 444, tothe computing device(s) 442. In at least one example, the localizationcomponent 420, the perception component 422, the planning component 424,the one or more system controllers 426, and the warning signal component428 may send their respective outputs to the computing device(s) 442 ata particular frequency, after a lapse of a predetermined period of time,in near real-time, etc.

In some examples, the vehicle 402 may send sensor data to the computingdevice(s) 442 via the network(s) 444. In some examples, the vehicle 402may receive sensor data from the computing device(s) 442 via thenetwork(s) 444. The sensor data may include raw sensor data and/orprocessed sensor data and/or representations of sensor data. In someexamples, the sensor data (raw or processed) may be sent and/or receivedas one or more log files.

The computing device(s) 442 may include processor(s) 448 and a memory440 storing a map component 450 and a sensor data processing component452. In some examples, the map component 450 may include functionalityto generate maps of various resolutions. In such examples, the mapcomponent 450 may send one or more maps to the vehicle computingdevice(s) 404 for navigational purposes. In various examples, the sensordata processing component 452 may be configured to receive data from oneor more remote sensors, such as sensor systems 406 and/or remote sensorsystem(s) 446. In some examples, the sensor data processing component452 may be configured to process the data and send processed sensor datato the vehicle computing device(s) 404, such as for use by the warningsignal component 428. In some examples, the sensor data processingcomponent 452 may be configured to send raw sensor data to the vehiclecomputing device(s) 404.

The processor(s) 416 of the vehicle 402 and the processor(s) 448 of thecomputing device(s) 442 may be any suitable processor capable ofexecuting instructions to process data and perform operations asdescribed herein. By way of example and not limitation, the processor(s)416 and 448 may comprise one or more Central Processing Units (CPUs),Graphics Processing Units (GPUs), or any other device or portion of adevice that processes electronic data to transform that electronic datainto other electronic data that may be stored in registers and/ormemory. In some examples, integrated circuits (e.g., ASICs, etc.), gatearrays (e.g., FPGAs, etc.), and other hardware devices may also beconsidered processors in so far as they are configured to implementencoded instructions.

Memory 418 and 440 are examples of non-transitory computer-readablemedia. The memory 418 and 440 may store an operating system and one ormore software applications, instructions, programs, and/or data toimplement the methods described herein and the functions attributed tothe various systems. In various implementations, the memory may beimplemented using any suitable memory technology, such as static randomaccess memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory capable ofstoring information. The architectures, systems, and individual elementsdescribed herein may include many other logical, programmatic, andphysical components, of which those shown in the accompanying figuresare merely examples that are related to the discussion herein.

In some instances, the memory 418 and 440 may include at least a workingmemory and a storage memory. For example, the working memory may be ahigh-speed memory of limited capacity (e.g., cache memory) that is usedfor storing data to be operated on by the processor(s) 416 and 440. Insome instances, the memory 418 and 440 may include a storage memory thatmay be a lower-speed memory of relatively large capacity that is usedfor long-term storage of data. In some cases, the processor(s) 416 and440 cannot operate directly on data that is stored in the storagememory, and data may need to be loaded into a working memory forperforming operations based on the data, as discussed herein.

It should be noted that while FIG. 4 is illustrated as a distributedsystem, in alternative examples, components of the vehicle 402 may beassociated with the computing device(s) 442 and/or components of thecomputing device(s) 442 may be associated with the vehicle 402. That is,the vehicle 402 may perform one or more of the functions associated withthe computing device(s) 442, and vice versa.

FIGS. 5-9 illustrate example processes in accordance with embodiments ofthe disclosure. These processes are illustrated as logical flow graphs,each operation of which represents a sequence of operations that may beimplemented in hardware, software, or a combination thereof. In thecontext of software, the operations represent computer-executableinstructions stored on one or more computer-readable storage media that,when executed by one or more processors, perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described operationsmay be combined in any order and/or in parallel to implement theprocesses.

FIG. 5 depicts an example process 500 for determining at least one of avolume or a frequency of a warning sound to emit toward an object, inaccordance with embodiments of the disclosure. For example, some or allof the process 500 may be performed by one or more components in FIG. 4,as described herein. For example, some or all of the process 500 may beperformed by the vehicle computing device(s) 404.

At operation 502, the process may include identifying an object within afirst threshold distance of a path of the vehicle. In various examples,an identification may include a vehicle computing system detecting theobject, such as by processing sensor data received from one or moresensors to determine that the object is present in the environment. Insome examples, the identification of the object may include determininga classification (e.g., type) of the object.

In various examples, the first threshold distance (e.g., 1 block, 4blocks, 400 meters, ½ mile, etc.) may be a pre-determined distance fromthe vehicle. In some examples, the first threshold distance may bedetermined based on a speed in which the vehicle is operating, a numberof objects present in the environment, one or more types (e.g., classes)of objects present in the environment, a zone in which the vehicle isoperating (e.g., school zone, construction zone, etc.), and/or otherfactors that could affect the warning signal determination.

At operation 504, the process may include determining a trajectoryassociated with the object. As discussed above, the vehicle computingsystem may process sensor data received from the sensor(s). The vehiclecomputing system may be configured to determine the trajectory of theobject based on observed and/or predicted behaviors. The trajectory ofthe object may include a direction, velocity and/or acceleration of theobject. In some examples, trajectory may be based, at least in part on aclassification of the object.

At operation 506, the process may include determining whether the objectwill be within a second threshold distance of the vehicle at a time inthe future. In some examples, the vehicle computing device may determinea distance between the vehicle and the object at the time in the futurebased on a comparison of trajectory of the vehicle and the trajectory ofthe object. In various examples, the second threshold distance may be apre-defined distance (e.g., 3 meters, 5 meters, 15 feet, 25 feet, etc.).In some examples, the second threshold distance may include a distancerelated to an intersection (i.e., 0 feet) between the trajectory of thevehicle and the trajectory of the object at a same or similar time inthe future. In some examples, the second threshold distance may bedetermined based on a speed at which the vehicle is operating, a speedof the object, a number of objects present in the environment, one ormore types of objects present in the environment, a zone in which thevehicle is operating, or the like. In various examples, the secondthreshold distance may be determined based on a likelihood (e.g.,probability) of conflict between the vehicle and the object.

Based on a determination that the object will not be within the secondthreshold distance of the vehicle at the time in the future (e.g., “No”at 506), the vehicle computing system may return to operation 502 andidentify another object within the first threshold distance of a path ofthe vehicle.

Based on a determination that the object will be within the secondthreshold distance of the vehicle at the time in the future (e.g., “Yes”at 506), the process may continue to operation 508, which may includedetermining a noise level proximate to the object. As discussed above,the noise level proximate to the object may be determined based on oneor more other noise generating objects (e.g., dynamic and/ornon-dynamic) located proximate to the object. In some examples, thenoise level may be determined based at least in part on a baseline noiselevel detected by the vehicle computing system in the environment. Invarious examples, the vehicle computing device may determine the noiselevel by extrapolating distinct and non-distinct noise events detectedby sensors of the vehicle based on a first distance between the noisegenerating object(s) and the vehicle and a second distance between thenoise generating object(s) and the object.

At operation 510, the process may include determining at least one of avolume or a frequency of a sound to emit. In some examples, the vehiclecomputing system may acquire sounds in the form of a Fourier analysis todetermine the at least one of the volume or the frequency of the soundto emit. In various examples, the vehicle computing system may determineone or more volumes and/or one or more frequencies of a sound (e.g.,warning signal) to emit. In such examples, the warning signal mayinclude a variable frequency and/or variable amplitude audio signal.

As discussed above, the volume(s) and/or frequencies may be determinedbased on various factors in the environment, such as a noise floorproximate to the object, a noise floor in the environment, a speed thevehicle is operating, type(s) of object(s) present in the environment, anumber of objects present in the environment, presence of a secondobject being located between the object and the vehicle in theenvironment, or the like. In some examples, the at least one of thevolume(s) and/or frequencies may be determined based on an urgency ofthe warning signal to be emitted, a likelihood of conflict, or the like.

At operation 512, the process may include emitting the sound toward theobject at the at least one of the volume (or set/range of volumes) orthe frequency (or set/range of frequencies). In various examples, thevehicle computing system may cause the sound to be emitted via one ormore speakers coupled to the vehicle. In some examples, the sound may beemitted in a directed toward the object, such as in a directed audiobeam. In some examples, the sound may be emitted at an angle up to andincluding 360 degrees around the vehicle.

FIG. 6 depicts an example process 600 for determining whether anemission of sound, such as that described in FIG. 5, was effective and,based on the effectiveness of the sound, ceasing emission of the soundor causing the vehicle to take an action to avoid a collision with theobject, in accordance with embodiments of the disclosure. For example,some or all of the process 600 may be performed by one or morecomponents in FIG. 4, as described herein. For example, some or all ofthe process 600 may be performed by the vehicle computing device(s) 404.

At operation 602, the process may include determining a secondtrajectory associated with the object. The second trajectory, similar tothe trajectory described at operation 504 of FIG. 5, may be determinedby processing sensor data received from the sensor(s). The secondtrajectory of the object may include a direction, velocity, and/oracceleration of the object.

At operation 604, the process may include determining whether the secondtrajectory differs from the first trajectory by a threshold amount(e.g., value). In various examples, the trajectories may differ in speedand/or direction. In such examples, the threshold amount may include anamount of speed change (e.g., 3 miles per hour to 1 mile per hour, 5miles per hour to 0 miles per hour, etc.) and/or a change in direction(e.g., adjusted angle above a threshold angle (e.g., 15 degrees, 25degrees, etc.), etc.). In various examples, the threshold amount maycorrespond to a determination that the object will not be within thesecond threshold distance of the path of the vehicle at a time in thefuture, as described above with regard to operation 506 of FIG. 5. Insome examples, the threshold amount may correspond to a likelihood(e.g., probability) of conflict between the vehicle and the object beingbelow a threshold level (e.g., threshold probability) of conflict.

If the second trajectory differs from the first trajectory by thethreshold amount (e.g., “yes” in the operation 604), the processcontinues to operation 606. At operation 606, the process may includeceasing emission of the sound.

If the second trajectory does not differ from the first trajectory bythe threshold amount (e.g., “no” in the operation 604), the processcontinues to operation 608. At operation 608, the process may includedetermining an action for the vehicle to take to avoid a collision withthe object. The action may include determining to emit a second warningsignal and/or causing the vehicle to alter the vehicle trajectory toavoid the collision. The second warning signal may include a volume (orset/range of volumes) and/or frequency (or set/range of frequencies)associated with a warning signal of increased urgency (e.g., low urgencyalert elevated to high urgency warning, low probability of conflictelevated to medium and/or high probability of conflict). An change(e.g., adjustment, alteration, etc.) to the vehicle trajectory mayinclude slowing or stopping the vehicle and/or changing the vehiclepath, such as a lane change right or left, altering a position in acurrent lane, swerving left or right, such as into a shoulder of theroadway, or the like.

At operation 610, the process may include causing the vehicle to takethe action. In various examples, the vehicle computing system may causethe second warning signal to be emitted from the speaker(s) coupled tothe vehicle. In some examples, the vehicle computing device may causethe vehicle, such as via the drive module 414 of FIG. 4, to take theaction.

FIG. 7 depicts an example process 700 for determining a volume of asound to emit toward an object, in accordance with embodiments of thedisclosure. For example, some or all of the process 700 may be performedby one or more components in FIG. 4, as described herein. For example,some or all of the process 700 may be performed by the vehicle computingdevice(s) 404.

At operation 702, the process may include receiving sensor data from oneor more sensors. The sensor(s) may include sensor(s) coupled to avehicle and/or remote sensor(s) in an environment. Based on the sensordata, a vehicle computing system may detect and/or identify apotentially conflicting object in the environment.

At operation 704, the process may include determining a distance to thepotentially conflicting object from the vehicle. The distance to thepotentially conflicting object may be determined based on at least someof the sensor data received at operation 702.

At operation 706, the process may include determining a predicted noisefloor proximate to the object. As discussed above, the noise floorproximate to the potentially conflicting object may be determined basedon a detection of one or more noise-producing objects proximate to thepotentially conflicting object. In some examples, the noise floorproximate to the potentially conflicting object may be based on distinctand non-distinct noise events occurring in the environment,classifications of proximate objects, relative distances and/or speedsto the proximate objects from the potentially conflicting object, a timeof day, a location of the potentially conflicting object, and the like.

At operation 708, the process may include applying a smoothing algorithmto the predicted noise floor based on the distance to the object. Invarious examples, an application of the smoothing algorithm may includedetermining a gain associated with the environment. In various examples,the smoothing algorithm may adjust an effect of distinct noise events tothe noise floor proximate to the object.

At operation 710, the process may include determining a behavior volumecode associated with a warning signal. The behavior volume code may bebased on an urgency of the warning, a likelihood of conflict, or thelike. In some examples, each behavior volume code may be associated witha range of volumes. For example, a behavior volume code may include anemergency volume range of 39-44 decibels above the noise floor, afriendly but clear and intentional volume range of 16-25 decibels abovethe noise floor, and an audible but subtle (e.g., subliminal) alertvolume range of 2-9 decibels above the noise floor.

At operation 712, the process may include determining a volume (or setand/or range of volumes) of warning signal to emit. In various examples,the volume(s) may be determined based on the input of the smoothed noisefloor proximate to the object (post smoothing algorithm) based on thedistance to the object and the determined behavior volume code.

FIG. 8 depicts an example process 800 for avoiding a collision between avehicle and an object in an environment by emitting a sound and/orcausing the vehicle to take an action to avoid the collision, inaccordance with embodiments of the disclosure. For example, some or allof the process 800 may be performed by one or more components in FIG. 4,as described herein. For example, some or all of the process 800 may beperformed by the vehicle computing device(s) 404.

At operation 802, the process may include determining a first trajectoryassociated with the vehicle in the environment. The first trajectoryassociated with the vehicle may include a velocity and/or accelerationof the vehicle along a vehicle path (e.g., direction of the vehicle).The first trajectory may be determined based on sensor data receivedfrom the sensor(s) and/or a planned path, velocity, and/or accelerationof the vehicle, such as that stored in a database of the vehiclecomputing system and sent to a drive module of the vehicle forexecution.

At operation 804, the process may include identifying an object in theenvironment. As discussed above, a vehicle computing system may detectand identify (e.g., classify) the object based on sensor data receivedfrom one or more sensors.

At operation 806, the process may include determining a secondtrajectory associated with the object. The vehicle computing system maydetermine the second trajectory based on the sensor data received fromthe sensor(s). The second trajectory may include a direction, velocity,and/or acceleration of the object.

At operation 808, the process may include determining, based on thefirst trajectory and the second trajectory, a possible collision betweenthe vehicle and the object. The possible collision may include aprobability (e.g., likelihood) of conflict between the vehicle and theobject. In various examples, the vehicle computing device may determinethat a possible collision may exist based on a determination that thevehicle on the first trajectory and the object on the second trajectorywill be within a threshold distance of one another at a time in thefuture.

At operation 810, the process may include causing the vehicle to performan action comprising at least one of emitting a sound (e.g., warningsignal) or yielding to the object. In various examples, the vehiclecomputing system may determine one or more volumes and/or one or morefrequencies of the sound to emit. In such examples, the volumes and/orfrequencies of the sound may be determined based on an urgency of thewarning signal, a probability of collision, a velocity and/oracceleration associated with the vehicle and/or the object, or the like.

As discussed above, the action may include yielding to the object, suchas by slowing or stopping the vehicle, changing lanes left or right,swerving away from the object, or the like. As discussed above withregard to FIG. 2, a determination to perform the action may be based ona future location of the potentially conflicting object being within asafety zone associated with the vehicle. The vehicle computing devicemay determine the action based on a determination that a particularaction may decrease the probability of collision below a threshold level(e.g., low probability).

FIG. 9 depicts an example process 900 for determining at least one of avolume or a frequency of a warning sound to emit toward an object, inaccordance with embodiments of the disclosure. For example, some or allof the process 900 may be performed by one or more components in FIG. 4,as described herein. For example, some or all of the process 900 may beperformed by the vehicle computing device(s) 404.

At operation 902, the process may include identifying an object in anenvironment associated with a vehicle. In various examples, anidentification may include a vehicle computing system detecting theobject, such as by processing sensor data received from one or moresensors to determine that the object is present in the environment. Insome examples, the identification of the object may include determininga classification (e.g., type) of the object.

At operation 904, the process may include determining a noise levelproximate to the object. As discussed above, the noise level proximateto the object may be determined based on one or more other noisegenerating objects located proximate to the object. In some examples,the noise level may be determined based on a baseline noise leveldetected by the vehicle computing system in the environment. In variousexamples, the vehicle computing device may determine the noise level byextrapolating distinct and non-distinct noise events detected by sensorsof the vehicle based on a first distance between the noise generatingobject(s) and the vehicle and a second distance between the noisegenerating object(s) and the object.

At operation 906, the process may include determining at least one of avolume (or set/range of volumes) or a frequency (or set/range offrequencies) of a sound to emit. In various examples, the vehiclecomputing system may determine one or more volumes and/or one or morefrequencies of a sound (e.g., warning signal) to emit. In such examples,the warning signal may include a variable frequency and/or variableamplitude audio signal.

As discussed above, the volume(s) and/or frequencies may be determinedbased on various factors in the environment, such as a noise floorproximate to the object, a noise floor in the environment, a speed thevehicle is operating, type(s) of object(s) present in the environment, anumber of objects present in the environment, presence of a secondobject being located between the object and the vehicle in theenvironment, or the like. In some examples, the at least one of thevolume(s) and/or frequencies may be determined based on an urgency ofthe warning signal to be emitted, a likelihood of conflict, or the like.

At operation 908, the process may include causing the sound to beemitted via a speaker coupled to the vehicle at the at least one of thevolume or the frequency. In various examples, the vehicle computingsystem may cause the sound to be emitted via one or more speakerscoupled to the vehicle. In some examples, the sound may be emitted in adirected toward the object, such as in a directed audio beam. In someexamples, the sound may be emitted at an angle up to and including 360degrees around the vehicle.

EXAMPLE CLAUSES

A: A system comprising: a sensor coupled to a vehicle; a speaker coupledto the vehicle; one or more processors; and one or morecomputer-readable media storing instructions that, when executed,configure the system to: identify, based on sensor data from the sensor,an object in an environment associated with the vehicle; determine anoise level proximate to the object, wherein the noise level is based atleast in part on noise events generated by one or more other objectslocated within a threshold distance of the object; determine, based atleast in part on the noise level, at least one of a volume or afrequency of a sound to emit; and emit, via the speaker, the sound atthe at least one of the volume or the frequency.

B: The system as paragraph A describes, wherein the instructions furthercause the system to: determine a first range of frequenciescorresponding to the noise events generated by the other objects,wherein the frequency is based at least in part on the first range offrequencies.

C: The system as either of paragraphs A or B describe, wherein the atleast one of the volume or the frequency are determined based on machinelearned outputs of noises emitted by identified objects proximate to theobject.

D: The system as any of paragraphs A-C describe, wherein the sound isemitted in a beam formed audio signal directed toward the object.

E: The system as any of paragraphs A-C describe, wherein theinstructions further cause the system to: determine, based on the sensordata, a first trajectory associated with the vehicle; determine, basedon the sensor data, a second trajectory associated with the object;determine a potential conflict between the vehicle and the object basedon the first trajectory and the second trajectory; determine an actionto take to avoid the potential conflict; and cause the vehicle to takethe action.

F: A computer-readable medium having thereon computer-executableinstructions that, responsive to execution, configure a computer toperform a system as any of paragraphs A-E describe.

G: A method comprising: identifying, based on sensor data from a sensorcoupled to a vehicle, an object in an environment associated with thevehicle; determining a noise level proximate to the object; determining,based at least in part on the noise level proximate to the object, atleast one of a volume or a frequency of a sound to emit; and causing thesound to be emitted via a speaker at the at least one of the volume orthe frequency.

H: The method as paragraph G describes, wherein the noise level is basedon noise events generated within a threshold distance of the object, themethod further comprising: determining a first range of frequenciescorresponding to the noise level proximate to the object, wherein thefrequency is based at least in part on the first range of frequencies.

I: The method as either of paragraphs G or H describe, wherein theobject is a first object, the method further comprising: identifying asecond object wherein determining the volume of the sound to emit isbased at least in part on the second object.

J: The method as any of paragraphs G-I describe, further comprising:determining a speed of the vehicle traveling through the environment,wherein determining the at least one of the volume or the frequency isbased at least in part on the speed of the vehicle.

K: The method as any of paragraphs G-J describe, further comprising:determining a trajectory associated with the object; and determining,based at least in part on the trajectory, a probability that the objectwill be within a threshold distance of the vehicle at a future time,wherein determining the at least one of the volume or the frequency ofthe sound to emit is based at least in part on the probability.

L: The method as any of paragraphs G-K describe, further comprising:determining a change in the trajectory associated with the object;determining, based at least in part on the change in the trajectory, tostop emitting the sound via the speaker.

M: The method as any of paragraphs G-L describe, wherein determining theat least one of the volume or the frequency is based in part on at leastone of: an occupancy of the vehicle; a road condition; a location of thevehicle in the environment; a speed of the vehicle in the environment; atime of day in which the vehicle is operating; a day in a week in whichthe vehicle is operating; or a weather condition in the environment.

N: The method as any of paragraphs G-M describe, further comprising:determining a first trajectory associated with the object at a firsttime, wherein the first trajectory corresponds to a potential conflictbetween the vehicle and the object; determining a second trajectoryassociated with the object at a second time; determining that adifference between the first trajectory and the second trajectory isless than a threshold value; determining at least one of a second volumeor a second frequency of a second sound to emit based at least in parton the difference between the first trajectory and the second trajectorybeing less than the threshold value; and causing the second sound to beemitted via the speaker at the at least one of the second volume or thesecond frequency.

O: The method as any of paragraphs G-N describe, further comprising:identify a classification associated with the object, whereindetermining the at least one of the volume or the frequency of the soundto emit is based at least in part on the classification associated withthe object.

P: A system or device comprising: a processor, and a computer-readablemedium coupled to the processor, the computer-readable medium includinginstructions to configure the processor to perform acomputer-implemented method as any of paragraphs G-O describe.

Q: A system or device comprising: a means for processing; and a meansfor storing coupled to the means for processing, the means for storingincluding instructions to configure one or more devices to perform acomputer-implemented method as any of paragraphs G-O describe.

R: A computer-readable medium having thereon computer-executableinstructions that, responsive to execution, configure a computer toperform a method as any one of paragraphs G-O describe.

S: One or more non-transitory computer-readable media storinginstructions that, when executed, cause a vehicle to perform operationscomprising: identifying, based on sensor data from a sensor coupled tothe vehicle, an object in an environment associated with the vehicle;determining a noise level proximate to the object; determining, based atleast in part on the noise level proximate to the object, at least oneof a volume or a frequency of a sound to emit; and causing the sound tobe emitted via a speaker coupled to the vehicle at the at least one ofthe volume or the frequency.

T: One or more non-transitory computer-readable media as paragraph Sdescribes, wherein the noise level is determined based on: determining afirst distance between the object and a first noise generating objectproximate to the object; accessing a database of noises to determine afirst noise associated with the first noise generating object; anddetermining a perceived noise level of the first noise by the objectbased at least in part on the first distance.

U: One or more non-transitory computer-readable media as paragraph Tdescribes, wherein the noise level is further determined based on:determining a second distance between the object and a second noisegenerating object proximate to the object; accessing the database ofnoises to determine a second noise associated with the second noisegenerating object; determining a second perceived noise level of thesecond noise by the object based at least in part on the seconddistance; and combining the first noise and the second noise todetermine the noise level proximate to the object.

V: One or more non-transitory computer-readable media as any ofparagraphs S-U describe, the operations further comprising: determininga speed of the vehicle traveling through the environment, whereindetermining the at least one of the volume or the frequency is based atleast in part on the speed of the vehicle.

W: One or more non-transitory computer-readable media as any ofparagraphs S-V describe, the operations further comprising: determininga trajectory associated with the object; and determining, based at leastin part on the trajectory, a probability that the object will be withina threshold distance of the vehicle at a future time, whereindetermining the at least one of the volume or the frequency of the soundto emit is based at least in part on the probability.

X: One or more non-transitory computer-readable media as paragraph Wdescribes, wherein the sound is a first sound and the trajectory is afirst trajectory associated with the object at a first time, theoperations further comprising: determining a second trajectoryassociated with the object at a second time; determining, based at leastin part on the second trajectory, a second probability that the objectwill be within a second threshold distance of the vehicle at a secondfuture time; determining at least one of a second volume or a secondfrequency of a second sound to emit based at least in part on the secondprobability; and causing the second sound to be emitted via the speakerat the at least one of the second volume or the second frequency.

Y: A system or device comprising: a processor; and a one or morenon-transitory computer-readable media as any of paragraphs S-Wdescribe.

While the example clauses described above are described with respect toone particular implementation, it should be understood that, in thecontext of this document, the content of the example clauses may also beimplemented via a method, device, system, a computer-readable medium,and/or another implementation.

CONCLUSION

While one or more examples of the techniques described herein have beendescribed, various alterations, additions, permutations and equivalentsthereof are included within the scope of the techniques describedherein.

In the description of examples, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific examples of the claimed subject matter. It is to be understoodthat other examples can be used and that changes or alterations, such asstructural changes, can be made. Such examples, changes or alterationsare not necessarily departures from the scope with respect to theintended claimed subject matter. While the steps herein may be presentedin a certain order, in some cases the ordering may be changed so thatcertain inputs are provided at different times or in a different orderwithout changing the function of the systems and methods described. Thedisclosed procedures could also be executed in different orders.Additionally, various computations that are herein need not be performedin the order disclosed, and other examples using alternative orderingsof the computations could be readily implemented. In addition to beingreordered, the computations could also be decomposed intosub-computations with the same results.

What is claimed is:
 1. A system comprising: a sensor coupled to avehicle; a speaker coupled to the vehicle; one or more processors; andone or more computer-readable media storing instructions that, whenexecuted, configure the system to: identify, based on sensor data fromthe sensor, a first object at a first location in an environmentassociated with the vehicle, wherein the first location is at a firstdistance from the vehicle; identify, based on the sensor data, a secondobject at a second location in the environment, wherein the secondlocation is a second distance from the first location associated withthe first object; determine that the second object is generating a noiseevent at a first volume based at least in part on a third distancebetween the vehicle and the second object; determine that seconddistance is equal to or less than a threshold distance; determine, basedat least in part on a determination that the second distance is equal toor less than the threshold distance, a noise level proximate to thefirst location associated with the first object, wherein the noise levelis determined based at least in part on the first volume and the seconddistance; determine, based at least in part on the noise level, at leastone of a second volume or a frequency of a sound to emit; and emit, viathe speaker, the sound at the at least one of the second volume or thefrequency.
 2. The system of claim 1, wherein the instructions furthercause the system to: determine a first range of frequenciescorresponding to noise events generated by other objects, wherein thefrequency is based at least in part on the first range of frequencies.3. The system of claim 1, wherein the at least one of the second volumeor the frequency are determined based on machine learned outputs ofnoises emitted by identified objects proximate to the first object. 4.The system of claim 1, wherein the sound is emitted in a beam formedaudio signal directed toward the first object.
 5. The system of claim 1,wherein the instructions further cause the system to: determine, basedon the sensor data, a first trajectory associated with the vehicle;determine, based on the sensor data, a second trajectory associated withthe object; determine a potential conflict between the vehicle and thefirst object based on the first trajectory and the second trajectory;determine an action to take to avoid the potential conflict; and causethe vehicle to take the action.
 6. A method comprising: identifying,based on sensor data from a sensor coupled to a vehicle, an object at afirst location in an environment associated with the vehicle, whereinthe first location is a first distance from the vehicle; identify, basedon the sensor data, a noise generating object at a second location inthe environment; determine a volume of a noise emitted by the noisegenerating object based at least in part on a second distance betweenthe second location and the vehicle; determining a noise level proximateto the first location associated with the object based at least in parton a third distance between the first location and the second locationand the volume of the noise emitted by the noise generating object;determining, based at least in part on the noise level proximate to thelocation associated with the object, at least one of a second volume ora frequency of a sound to emit; and causing the sound to be emitted viaa speaker at the at least one of the second volume or the frequency. 7.The method of claim 6, wherein the noise level is based on noise eventsgenerated within a threshold distance of the object, the method furthercomprising: determining a first range of frequencies corresponding tothe noise level proximate to the object, wherein the frequency is basedat least in part on the first range of frequencies.
 8. The method ofclaim 6, wherein the object is a first object, the method furthercomprising: identifying a second object, wherein determining the volumeof the sound to emit is based at least in part on the second object. 9.The method of claim 6, further comprising: determining a speed of thevehicle traveling through the environment, wherein determining the atleast one of the second volume or the frequency is based at least inpart on the speed of the vehicle.
 10. The method of claim 6, furthercomprising: determining a trajectory associated with the object; anddetermining, based at least in part on the trajectory, a probabilitythat the object will be within a threshold distance of the vehicle at afuture time, wherein determining the at least one of the second volumeor the frequency of the sound to emit is based at least in part on theprobability.
 11. The method of claim 10, further comprising: determininga change in the trajectory associated with the object; and determining,based at least in part on the change in the trajectory, to stop emittingthe sound via the speaker.
 12. The method of claim 6, whereindetermining the at least one of the second volume or the frequency isbased in part on at least one of: an occupancy of the vehicle; a roadcondition; a location of the vehicle in the environment; a speed of thevehicle in the environment; a time of day in which the vehicle isoperating; a day in a week in which the vehicle is operating; or aweather condition in the environment.
 13. The method of claim 6, furthercomprising: determining a first trajectory associated with the object ata first time, wherein the first trajectory corresponds to a potentialconflict between the vehicle and the object; determining a secondtrajectory associated with the object at a second time; determining thata difference between the first trajectory and the second trajectory isless than a threshold value; determining at least one of a third volumeor a second frequency of a second sound to emit based at least in parton the difference between the first trajectory and the second trajectorybeing less than the threshold value; and causing the second sound to beemitted via the speaker at the at least one of the third volume or thesecond frequency.
 14. The method of claim 6, further comprising:identify a classification associated with the object, whereindetermining the at least one of the second volume or the frequency ofthe sound to emit is based at least in part on the classificationassociated with the object.
 15. One or more non-transitorycomputer-readable media storing instructions that, when executed, causea vehicle to perform operations comprising: identifying, based on sensordata from a sensor coupled to the vehicle, an object at a first locationin an environment associated with the vehicle, wherein the firstlocation is a first distance from the vehicle; identify, based on thesensor data, a noise generating object at a second location in theenvironment; determine a volume of a noise emitted by the noisegenerating object based at least in part on a second distance betweenthe second location and the vehicle; determining a noise level proximateto the first location associated with the object based at least in parton a third distance between the first location and the second locationand the volume of the noise emitted by the noise generating object;determining, based at least in part on the noise level proximate to thefirst location associated with the object, at least one of a secondvolume or a frequency of a sound to emit; and causing the sound to beemitted via a speaker coupled to the vehicle at the at least one of thevolume or the frequency.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein the noise level isdetermined based on: accessing a database of noises to determine thevolume of the noise associated with the first noise generating object;and determining a perceived noise level of the noise by the object basedat least in part on the second distance.
 17. The one or morenon-transitory computer-readable media of claim 16, wherein the noisegenerating object is a first noise generating object, the noise is afirst noise, and the noise level is further determined based on:determining a fourth distance between the object and a second noisegenerating object proximate to the object; accessing the database ofnoises to determine a second noise associated with the second noisegenerating object; determining a second perceived noise level of thesecond noise by the object based at least in part on the fourthdistance; and combining the first noise and the second noise todetermine the noise level proximate to the object.
 18. The one or morenon-transitory computer-readable media of claim 15, the operationsfurther comprising: determining a speed of the vehicle traveling throughthe environment, wherein determining the at least one of the secondvolume or the frequency is based at least in part on the speed of thevehicle.
 19. The one or more non-transitory computer-readable media ofclaim 15, the operations further comprising: determining a trajectoryassociated with the object; and determining, based at least in part onthe trajectory, a probability that the object will be within a thresholddistance of the vehicle at a future time, wherein determining the atleast one of the second volume or the frequency of the sound to emit isbased at least in part on the probability.
 20. The one or morenon-transitory computer-readable media of claim 19, wherein the sound isa first sound and the trajectory is a first trajectory associated withthe object at a first time, the operations further comprising:determining a second trajectory associated with the object at a secondtime; determining, based at least in part on the second trajectory, asecond probability that the object will be within a second thresholddistance of the vehicle at a second future time; determining at leastone of a third volume or a second frequency of a second sound to emitbased at least in part on the second probability; and causing the secondsound to be emitted via the speaker at the at least one of the secondvolume or the second frequency.